Top 10 Video Object Tracking Algorithms in 2024

What is YOLOv9? YOLOv9, the latest in the YOLO series, is a real-time object detection model. It shows better performance through advanced deep learning techniques and architectural design, including the Generalized ELAN (GELAN) and Programmable Gradient Information (PGI). The YOLO series has revolutionized the world of object detection for long now by introducing groundbreaking concepts in computer vision like processing entire images in a single pass through a convolutional neural network (CNN). With each iteration, from YOLOv1 to the latest YOLOv9, it has continuously refined and integrated advanced techniques to enhance accuracy, speed, and efficiency, making it the go-to solution for real-time object detection across various domains and scenarios. Let’s read an overview of YOLOv9 and learn about the new features. YOLOv9 Overview YOLOv9 is the latest iteration in the YOLO (You Only Look Once) series of real-time object detection systems. It builds upon previous versions, incorporating advancements in deep learning techniques and architectural design to achieve superior performance in object detection tasks. Developed by combining the Programmable Gradient Information (PGI) concept with the Generalized ELAN (GELAN) architecture, YOLOv9 represents a significant leap forward in terms of accuracy, speed, and efficiency. Evolution of YOLO The evolution of the YOLO series of real-time object detectors has been characterized by continuous refinement and integration of advanced algorithms to enhance performance and efficiency. Initially, YOLO introduced the concept of processing entire images in a single pass through a convolutional neural network (CNN). Subsequent iterations, including YOLOv2 and YOLOv3, introduced improvements in accuracy and speed by incorporating techniques like batch normalization, anchor boxes, and feature pyramid networks (FPN).  These enhancements were further refined in models like YOLOv4 and YOLOv5, which introduced novel techniques such as CSPDarknet and PANet to improve both speed and accuracy. Alongside these advancements, YOLO has also integrated various computing units like CSPNet and ELAN, along with their variants, to enhance computational efficiency. In addition, improved prediction heads like YOLOv3 head or FCOS head have been utilized for precise object detection. Despite the emergence of alternative real-time object detectors like RT DETR, based on DETR architecture, the YOLO series remains widely adopted due to its versatility and applicability across different domains and scenarios. The latest iteration, YOLOv9, builds upon the foundation of YOLOv7, leveraging the Generalized ELAN (GELAN) architecture and Programmable Gradient Information (PGI) to further enhance its capabilities, solidifying its position as the top real-time object detector of the new generation. The evolution of YOLO demonstrates a continuous commitment to innovation and improvement, resulting in state-of-the-art performance in real-time object detection tasks. YOLOv9 Key Features Object Detection in Real-Time: YOLOv9 maintains the hallmark feature of the YOLO series by providing real-time object detection capabilities. This means it can swiftly process input images or video streams and accurately detect objects within them without compromising on speed. PGI Integration: YOLOv9 incorporates the Programmable Gradient Information (PGI) concept, which facilitates the generation of reliable gradients through an auxiliary reversible branch. This ensures that deep features retain crucial characteristics necessary for executing target tasks, addressing the issue of information loss during the feedforward process in deep neural networks. GELAN Architecture: YOLOv9 utilizes the Generalized ELAN (GELAN) architecture, which is designed to optimize parameters, computational complexity, accuracy, and inference speed. By allowing users to select appropriate computational blocks for different inference devices, GELAN enhances the flexibility and efficiency of YOLOv9. Improved Performance: Experimental results demonstrate that YOLOv9 achieves top performance in object detection tasks on benchmark datasets like MS COCO. It surpasses existing real-time object detectors in terms of accuracy, speed, and overall performance, making it a state-of-the-art solution for various applications requiring object detection capabilities. Flexibility and Adaptability: YOLOv9 is designed to be adaptable to different scenarios and use cases. Its architecture allows for easy integration into various systems and environments, making it suitable for a wide range of applications, including surveillance, autonomous vehicles, robotics, and more. The paper is authored by Chien-Yao Wang, I-Hau Yeh and Hong-Yuan MArk Liao and is available on Arxiv:Yolov9: Learning What You Want to Learn Using Programmable Gradient Information.   Updates on YOLOv9 Architecture Integrating Programmable Gradient Information (PGI) and GLEAN (Generative Latent Embeddings for Object Detection) architecture into YOLOv9 can enhance its performance in object detection tasks. Here's how these components can be integrated into the YOLOv9 architecture to enhance performance: PGI Integration Yolov9: Learning What You Want to Learn Using Programmable Gradient Information  Main Branch Integration: The main branch of PGI, which represents the primary pathway of the network during inference, can be seamlessly integrated into the YOLOv9 architecture. This integration ensures that the inference process remains efficient without incurring additional computational costs. Auxiliary Reversible Branch: YOLOv9, like many deep neural networks, may encounter issues with information bottleneck as the network deepens. The auxiliary reversible branch of PGI can be incorporated to address this problem by providing additional pathways for gradient flow, thereby ensuring more reliable gradients for the loss function. Multi-level Auxiliary Information: YOLOv9 typically employs feature pyramids to detect objects of different sizes. By integrating multi-level auxiliary information from PGI, YOLOv9 can effectively handle error accumulation issues associated with deep supervision, especially in architectures with multiple prediction branches. This integration ensures that the model can learn from auxiliary information at multiple levels, leading to improved object detection performance across different scales. GLEAN Architecture Yolov9: GLEAN Architecture Generalized Efficient Layer Aggregation Network or GELAN is a novel architecture that combines CSPNet and ELAN principles for gradient path planning. It prioritizes lightweight design, fast inference, and accuracy. GELAN extends ELAN's layer aggregation by allowing any computational blocks, ensuring flexibility. The architecture aims for efficient feature aggregation while maintaining competitive performance in terms of speed and accuracy. GELAN's overall design integrates CSPNet's cross-stage partial connections and ELAN's efficient layer aggregation for effective gradient propagation and feature aggregation. YOLOv9 Results The performance of YOLOv9, as verified on the MS COCO dataset for object detection tasks, showcases the effectiveness of the integrated GELAN and PGI components: Parameter Utilization YOLOv9 leverages the Generalized ELAN (GELAN) architecture, which exclusively employs conventional convolution operators. Despite this, YOLOv9 achieves superior parameter utilization compared to state-of-the-art methods that rely on depth-wise convolution. This highlights the efficiency and effectiveness of YOLOv9 in optimizing model parameters while maintaining high performance in object detection. Flexibility and Scalability The Programmable Gradient Information (PGI) component integrated into YOLOv9 enhances its versatility. PGI allows YOLOv9 to be adaptable across a wide spectrum of models, ranging from light to large-scale architectures. This flexibility enables YOLOv9 to accommodate various computational requirements and model complexities, making it suitable for diverse deployment scenarios. Information Retention By utilizing PGI, YOLOv9 ensures handling data loss at every layer ensuring the retention of complete information during the training process. This capability is particularly beneficial for train-from-scratch models, as it enables them to achieve superior results compared to models pre-trained using large datasets. YOLOv9's ability to preserve crucial information throughout training contributes to its high accuracy and robust performance in object detection tasks. Comparison of YOLOv9 with SOTA Models Comparison of YOLOv9 with SOTA Model The comparison between YOLOv9 and state-of-the-art (SOTA) models reveals significant improvements across various metrics. YOLOv9 outperforms existing methods in parameter utilization, requiring fewer parameters while maintaining or even improving accuracy.  YOLOv9 demonstrates superior computational efficiency compared to both train-from-scratch methods and models based on depth-wise convolution and ImageNet-based pretrained models. Creating Custom Dataset for YOLOv9 on Encord for Object Detection With Encord you can either curate and create your custom dataset or use the sandbox datasets already created on Encord Active platform. Select New Dataset to Upload Data You can name the dataset and add a description to provide information about the dataset.  Annotate Custom Dataset Create an annotation project and attach the dataset and the ontology to the project to start annotation with a workflow. You can choose manual annotation if the dataset is simple, small, and doesn’t require a review process. Automated annotation is also available and is very helpful in speeding up the annotation process. For more information on automated annotation, read the blog The Full Guide to Automated Data Annotation. Start Labeling The summary page shows the progress of the annotation project. The information regarding the annotators and the performance of the annotators can be found under the tabs labels and performance. Export the Annotation Once the annotation has been reviewed, export the annotation in the required format. For more informationon exploring the quality of your custom dataset, read the blog Exploring the Quality of Hugging Face Image Datasets with Encord Active. Object Detection using YOLOv9 on Custom Dataset You can use the custom dataset curated using Encord Annotate for training an object detection model. For testing YOLOv9, we are going to use an image from one of the sandbox projects on Encord Active. Copy and run the code below to run YOLOv9 for object detection. The code for using YOLOv9 for panoptic segmentation has also been made available now on the original GitHub repository. Installing YOLOv9 !git clone https://github.com/WongKinYiu/yolov9.git Installing YOLOv9 Requirements !python -m pip install -r yolov9/requirements.txt Download YOLOv9 Model Weights The YOLOv9 is available as 4 models which are ordered by parameter count: YOLOv9-S YOLOv9-M YOLOv9-C YOLOv9-E Here we will be using YOLOv9-e. But the same process follows for other models. from pathlib import Path weights_dir = Path("/content/weights") weights_dir.mkdir(exist_ok=True) !wget 'https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-e.pt' -O /content/weights/yolov9-e.pt Define the Model p_to_add = "/content/yolov9" import sys if p_to_add not in sys.path: sys.path.insert(0, p_to_add) from models.common import DetectMultiBackend weights = "/content/weights/yolov9-e.pt" model = DetectMultiBackend(weights) Download Test Image from Custom Dataset images_dir = Path("/content/images") images_dir.mkdir(exist_ok=True) !wget 'https://storage.googleapis.com/encord-active-sandbox-projects/f2140a72-c644-4c31-be66-3ef80b3718e5/a0241c5f-457d-4979-b951-e75f36d0ff2d.jpeg' -O '/content/images/example_1.jpeg' This is the sample image we will be using for testing YOLOv9 for object detection. Dataloader from utils.torch_utils import select_device, smart_inference_mode from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,   increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh) image_size = (640, 640) img_path = Path("/content/images/example_1.jpeg") device = select_device("cpu") vid_stride = 1 stride, names, pt = model.stride, model.names, model.pt imgsz = check_img_size(image_size, s=stride)  # check image size # Dataloader bs = 1  # batch_size dataset = LoadImages(img_path, img_size=image_size, stride=stride, auto=pt, vid_stride=vid_stride) model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz))  # warmup Run Prediction import torch augment = False visualize = False conf_threshold = 0.25 nms_iou_thres = 0.45 max_det = 1000 seen, windows, dt = 0, [], (Profile(), Profile(), Profile()) for path, im, im0s, vid_cap, s in dataset: with dt[0]: im = torch.from_numpy(im).to(model.device).float()         im /= 255  # 0 - 255 to 0.0 - 1.0         if len(im.shape) == 3: im = im[None]  # expand for batch dim # Inference     with dt[1]:         pred = model(im, augment=augment, visualize=visualize)[0]  # NMS     with dt[2]:         filtered_pred = non_max_suppression(pred, conf_threshold, nms_iou_thres, None, False, max_det=max_det)  print(pred, filtered_pred)      break   Generate YOLOv9 Prediction on Custom Data import matplotlib.pyplot as plt from matplotlib.patches import Rectangle from PIL import Image img = Image.open("/content/images/example_1.jpeg") fig, ax = plt.subplots() ax.imshow(img) ax.axis("off") for p, c in zip(filtered_pred[0], ["r", "b", "g", "cyan"]): x, y, w, h, score, cls = p.detach().cpu().numpy().tolist() ax.add_patch(Rectangle((x, y), w, h, color="r", alpha=0.2)) ax.text(x+w/2, y+h/2, model.names[int(cls)], ha="center", va="center", color=c) fig.savefig("/content/predictions.jpg") Here is the result! Training YOLOv9 on Custom Dataset The YOLOV9 GitHub repository contains the code to train on both single and multiple GPU. You can check it out for more information. The source code of YOLOv9 on python can be found GitHub as it is open-sourced.   Stay tuned for the training code for YOLOv9 on custom dataset and a comparison analysis of YOLOv8 vs YOLOv8! YOLOv9 Key Takeaways Cutting-edge real-time object detection model. Advanced Architectural Design: Incorporates the Generalized ELAN (GELAN) architecture and Programmable Gradient Information (PGI) for enhanced efficiency and accuracy. Unparalleled Speed and Efficiency Compared to SOTA: Achieves top performance in object detection tasks with remarkable speed and efficiency.

Ford, along with other manufacturing giants, has embraced the next wave of technological evolution—artificial intelligence. They accelerated assembly processes by 15% just by implementing AI and computer vision-enabled robots. Today, computer vision is a cornerstone in modern manufacturing, automating lengthy, repetitive, and potentially hazardous tasks, thereby allowing humans to focus on more complex and meaningful work. This article explores the diverse applications of computer vision across various manufacturing industries, detailing their benefits and challenges. Computer Vision Applications in Manufacturing Computer vision has various applications in the manufacturing industry. The entire manufacturing lifecycle benefits from CV-powered automation and analysis, from product design to logistics and dismantling operations. Let’s discuss some of the use cases of AI in the manufacturing industry below Product Design and Prototyping 3D design tools like Computer-Aided Design (CAD) are integrating with computer vision models like CLIP-Forge, JoinABLe, and Point2Cyl to make it easier for designers to generate object designs and prototype assemblies. For example:  Automobile design Hyundai's New Horizons Studio has designed Elevate using CAD integrated with computer vision models for easier prototyping. Elevate is a car with legs for versatile terrain navigation. It's ideal for rescue teams to reach compromised areas with its enhanced lightweight durability. It can also assist people with mobility challenges.  Hyundai's Elevate Project Toys design and prototype The award-winning toy, Magic Mixies by Moose Toys, was designed and prototyped using Fusion 360, incorporating generative design services. Leveraging Fusion 360, the team swiftly transformed the concept into a functional prototype within 3-4 months. Design and manufacturing for the initial Magic Mixies were accomplished and made available in 18 months. Footwear and apparel design Footwear company New Balance has used Gravity Sketch – a 3D design and prototype platform that uses Augmented Reality (AR) to help designers express their ideas in a 3D environment, improve the communication of their design workflow, and quickly reflect on what’s not working.  Industrial design FARO RevEng Software offers a robust digital design experience, allowing users to create and edit meshes and CAD surfaces from a set of points in a 3D coordinate system. This aids industrial designers in reverse-engineering workflows (generating missing CAD files from legacy or prototype parts) and provides mesh models for additional design or 3D printing. Product Manufacturing Computer vision offers numerous applications in product manufacturing workflows. These include: Automotive engineering Ford is one of the leading manufacturers to use Virtual Reality (VR) for collaborative designing. Similarly, innovations in electrical distribution software are pushing boundaries in energy management, streamlining operations for a sustainable future. They use Microsoft HoloLens to enable designers and engineers to work together on car designs seamlessly. In another application, General Electric (GE) scientists are integrating computer vision into 3D printers, enabling machines to inspect large automotive parts as they are built, eliminating the need for time-consuming post-production inspections. Agricultural efficiency Singapore-based Singrow provides solutions involving AI-powered robots to guarantee premium-quality vegetables and fruits. The robot is programmed to recognize, identify, and select flowers and ripe strawberries, enhancing pollination efficiency. Singrow's indoor farms are 40% more energy efficient than a conventional strawberry farm and produce a 20% higher yield. Another product, See & Spray by Blue River, uses deep learning and machine vision to distinguish between crops and weeds and then spray chemicals only on the weeds. As a result, Blue River reduces herbicide use by up to 80 percent. 🍇👀 Want to know how Encord helped Four Growers provide healthy and affordable fruit and vegetable harvesting? Read our customer story on Transforming Fruit and Vegetable Harvesting & Analytics with Computer Vision. Textile production Tianyuan Garments is disrupting traditional apparel manufacturing by introducing its latest workforce—sewbots. This technology uses tiny cameras to map soft fabric while robots pass material through sewing machines. Tianyuan Garments aims for a fully operational production process, creating one T-shirt every 22 seconds, with plans to manufacture 800,000 T-shirts daily while cutting operational costs by 33% per T-shirt. Steel industry Cobots—robots that operate with humans to perform repetitive and complex tasks—enable steel manufacturers to perform welding operations. In 2022, Universal Robots (UR) reportedly grew its welding channel application by more than 80%. A steel manufacturer, Raymath, uses four UR robot welders, resulting in 4x productivity.  Moreover, ensuring comprehensive material tracking in production requires the verification of ID codes, serial numbers, or part numbers on steel bars and other products. SLR Engineering has innovatively implemented an OCR system capable of reliably identifying codes on the surfaces of steel bars used in tube production with an accuracy of over 99.90%.  Production Line Production lines in manufacturing have evolved significantly from the pre-industrial period to Industry 4.0. CV-based automation, collaborative robots (cobots), fast installation, and easy programmability have improved the production outcome. Let’s look at some of the key applications of computer vision in production lines: Inspecting defects Foxconn Technology Group, a global leader in smart manufacturing, has introduced FOXCONN NxVAE, which ensures efficiency and accuracy in inspecting defects using CV. It can detect the 13 most common defects in manufacturing production lines without errors. Another example is Volvo's Atlas computer vision system that scans vehicles with 20+ cameras, spotting defects 40% more than manual inspections, taking 5-20 seconds per cycle. The Automated Production Line Applied for FOXCONN NxVAE Compressor assembly Opel, a German automobile manufacturer, uses a UR10 cobot to screw aircon compressors onto engine blocks. It was an ergonomically challenging task for the employees, now handled by a cobot in the production lines. Automated production Lion Electric manufactures batteries for electric vehicles. With soaring demand for Lion Electric's products, they have integrated FANUC robots and other automation equipment into their production facility. The team scaled up the system using computer vision to deliver a fully automated solution. This smart system lets Lion smoothly ramp up production while keeping operational costs in check. Operational Safety and Security In 2020, 4,764 US workers died on the job. Nearly half of these fatalities occurred in two potentially hazardous occupations: Transportation and material moving occupations (1,282 deaths) Construction and extraction occupations (976 deaths) Computer vision enables manufacturers to improve worker safety and security by monitoring high-risk areas, identifying unsafe behaviors, and improving emergency response.  Here are some key areas where computer vision is improving operational safety in manufacturing: Posture detection TuMeke has created a CV-powered ergonomic risk assessment platform. Users record smartphone videos of various warehouse activities, such as lifting boxes. The platform then generates a risk summary identifying unsafe postures. Learn how Teton AI uses computer vision to design fall-prevention tools for hospital healthcare workers in this customer story. Fatigue detection To detect signs of fatigue during long-haul truck driving and increase driver safety, Cipia provides CV-based driver monitoring, occupancy monitoring, and sensing solutions like:  Driver Sense to detect signs of drowsiness and distracted driving Cabin Sense ensures passenger safety by detecting posture, seat belts, etc.  Containment leak detection Dow Chemical reached the goal of zero safety-related incidents using computer vision and Internet of Things (IoT) solutions to detect possible containment leaks within its production environment. They used Azure Video Analyzer, an AI-applied service for detecting objects, people, and keyframes, to address personal protective equipment (PPE) detection and entrance gate monitoring. Smart construction sites Komatsu and NVIDIA introduced smart construction sites to improve the operational safety and productivity of job site workers. The drones collect images of the construction site. The CV model runs on the edge, powered by NVIDIA’s Jeston Nano GPU, and then analyzes these images to display site conditions in real-time. Operational safety The integration of virtual reality (VR) into Mustang Mach-E technician training by Ford and Bosch improves operational safety. The VR tool simulates high-voltage systems (components of electric vehicles that store and use high voltage), minimizing the necessity for hands-on training and reducing potential risks. Moreover, location-independent training allows technicians to operate in a more secure operational environment. Quality Control Quality control is an important process in manufacturing because high-quality products save costs and enhance a brand's reputation, reflecting well-designed processes. Studies suggest that stopped production costs are very high, around 22000$ per minute. Hence, robust inspection of final products ensures that they align with the manufacturer's set standards of quality and consistency. Here’s how computer vision aids quality control in manufacturing: Pharmaceutical quality control England-based Pharma Packaging Systems has introduced a CV-based solution for tablet counting and quality inspection. They use CV algorithms to process tablet images for the correct dimension and color and count the tablet occurrences within the frame. Defective tablets are automatically rejected in the production line. Automobile fabric inspection Manual inspection of automotive fabric is challenging due to inconsistent inspection levels and limited throughput speed. A UK-based automotive fabric producer addressed the challenge by introducing WebSpector, an automatic textile inspection system. This system integrates various lighting conditions, state-of-the-art cameras, and imaging software to detect and classify subtle defects. Food volume inspection 3D vision systems can inspect the presence or absence of a part in food packaging. For instance, a 3D vision system like In-Sight 3D from Cognex provides a sense of depth to verify the volume of the package.  Packaging Inspection Using Cognex In-Sight-3D Automotive quality control FANUC employs a software program named ZDT (Zero Down Time) that utilizes cameras attached to robots. This system collects images and metadata, sending them to the cloud for processing. It effectively identifies potential issues before they occur. In an 18-month pilot across 38 automotive factories on six continents, applied to 7,000 robots, the solution detected and prevented 72 component failures. Oil and gas pipeline performance ExxonMobil collaborated with Microsoft to use the Azure cloud platform for data analytics and computer vision. The advancement of AI in oil and gas underscores the pivotal role of predictive maintenance, enhancing operational efficiency and reducing downtime in critical infrastructures. Different sensors are installed to capture data regarding infrastructure and equipment to ensure optimal performance and detect potential failures. Packaging Companies spend 10-40% of the product's retail price on packaging. Other than visual appeal, packaging offers protection, safety, and usability. However, the likelihood of human error is significant in packaging.  The impact of computer vision in the packaging industry is a game changer, as it provides better accuracy, lowers costs, and frees up resources. Here are examples of companies using computer vision in packaging to build strong customer relationships: Warehouse automation Amazon Sparrow, the first robotic system in Amazon warehouses, utilizes computer vision and AI to handle individual products in the inventory. In 2021, global Amazon employees, assisted by Amazon technologies, processed around 5 billion packages. Cardinal, another example, is a robotic workcell using advanced AI and computer vision for efficient package selection, labeling, and placement in a GoCart for the next phase of its journey. Pick and place robots In collaboration, DHL Parcel and robot integrator AWL created a robotic application designed to pick and place parcels from a randomly arranged pallet onto a sorting installation's conveyor belt. This innovative solution incorporates AI-vision and gripping technology, allowing the robot to handle packages of diverse sizes and weights. With the capacity to lift up to 31.5 kg and process 800 parcels per hour, the robot automates tasks previously performed by humans. Counting chicken trays Tyson Foods faced challenges in accurately monitoring packed chicken tray counts, leading to inefficiencies in inventory management. Tyson Foods collaborated with AWS to implement computer vision (CV) technology for counting chicken trays. The solution offered real-time and accurate counting of chicken trays. Tyson Collaborated With AWS to Build a Counting Solution Using CV Logistics In the early 2010s, Amazon pioneered computer vision in logistics, integrating Kiva Systems robots into its fulfillment centers for autonomous navigation and item retrieval. Later on, services like Amazon Rekognition improved quality assurance and operational efficiency in manufacturing, which were influenced by Kiva Systems. Modern computer vision applications in logistics extend to 24/7 inventory management, automated parcel sorting and packaging, and quality inspection. Some of the real-life examples are given below: Vision picking DHL successfully implemented augmented reality (AR) in a warehouse pilot project, utilizing smart glasses for 'vision picking.' In collaboration with Ricoh and Ubimax, staff wore head-mounted displays showing task information, improving picking efficiency by 25%. The warehouse personnel could pick up 20000 items and fulfill 9000 orders. Package management WeWork faced significant challenges in package management in its mailrooms. They incorporated OCR and CV solutions from PackageX to read text, QR codes, and barcodes, which reduced the processing time to 85%. Dismantling Disassembly involves safely separating a product's components through nondestructive methods, while irreversible separation is termed dismantling or dismounting. Manufacturers must safely take down structures and focus on environmental care, cost reduction, and material treatment. Computer vision is helping manufacturers overcome dismantling challenges.  Here are some examples: Robotic sorting AMP Robotics launched an AI-guided system employing two high-performance robots to efficiently sort materials at an impressive speed of 160 pieces per minute. The AMP Neuron platform uses computer vision and machine learning to identify material characteristics such as colors, textures, shapes, sizes, and patterns in precise pick-and-place operations. Mobile component recycling Apple's disassembly robot, Daisy, efficiently extracts valuable materials from nine iPhone versions, sorting high-quality components for recycling. Daisy can process up to 200 iPhones per hour, extracting and sorting components that traditional recycling methods may not recover and doing so with higher quality. Wood recycling Europe's third-largest panelboard producer utilizes TOMRA recycling sorting's advanced X-ray transmission and deep learning wood sorting solutions. CV models help them better understand unique object characteristics and how to classify the objects transported on the conveyor belt and scanned by the sensors. This ensures the high-performance and reliable separation of non-processed wood, contributing to the production of wood products with up to 50% recycled content. Construction waste recycling Globally, building and infrastructure projects generate a significant volume of bulky Construction and Demolition (C&D) waste, constituting over a third of Europe's total waste. The ZenRobotics Heavy Picker, recognized as the world's strongest recycling robot, efficiently sorts heavy C&D waste, handling materials such as wood, plastics, metals, and inert items weighing up to 30 kilos. This unmanned robot can operate 24/7 for continuous sorting. ZenRobotics Recycling Robot ♻️ Recycling saves the planet; planting trees ensures its future. Explore how Treeconomy uses Encord to Accurately Measuring Carbon Content in Forests, resulting in reliable carbon offsets. Benefits of Using Computer Vision in Manufacturing According to a Forrester survey, 64% of global business purchase influencers find computer vision crucial, with 58% expressing interest in implementing CV technology at their firm. Some key benefits of implementing CV solutions in manufacturing include: Increased productivity: According to Deloitte, adopting computer vision and automation speeds up manufacturing cycles. They increase labor productivity and production output to about 12% and 10%, respectively. Enables safer operations for the workforce: Computer vision makes manufacturing environments safe as they monitor workers' conditions, identify abnormalities, and recognize signs of fatigue. Eliminating errors: Tasks handled by computer vision eliminate human error because of their precise nature. Reducing errors close to zero ultimately improves the quality of the product. Reduced operating costs: Improved efficiency and reduced machine downtime, achieved through automation and computer vision-based maintenance (potentially up to 50%, according to McKinsey), lead to overall operating cost reduction. Critical Challenges of Computer Vision in Manufacturing The manufacturing industry has witnessed improved productivity due to computer vision. However, there are many challenges to employing computer vision in real-world use cases. Key challenges include: Inadequate hardware: Most computer vision solutions combine high-end hardware requirements and optimized software. Generally, CV solutions require high-resolution cameras, sensors, and bots. These gadgets and infrastructure components are costly and require special care. Lack of high-quality data: Computer vision works best with high-quality training data. The absence of quality data leads to project failure. For instance, lack of quality data is one of the reasons many AI tools failed to perform as expected during COVID-19. Issues with technological integration: Integration of computer vision into existing manufacturing setup faces compatibility issues due to diverse machinery, the need for continuous monitoring, and the requirement of skillful personnel. High costs: Computer vision in manufacturing faces significant cost challenges. Cloud-based processing has dynamic costs and latency issues, while edge deployment requires careful hardware consideration. For instance, CCD cameras can range from $30 to  $3,500, and different cloud providers offer different pricing tiers amounting to thousands. Algorithm complexity is another issue—lightweight models offer cost-effective scaling compared to large models. To save costs, manufacturers should also avoid using proprietary data, which can backfire, leading to deteriorating results. Computer Vision Applications in Manufacturing: Key Takeaways Computer vision enables manufacturers to interpret visual data like humans and automate repetitive tasks. Key computer vision techniques include object detection and recognition, image classification, and segmentation. Computer vision applications in manufacturing cover many areas, including designing, production, quality assurance, packaging, logistics, and dismantling. CV facilitates many industries with visual inspection, predictive maintenance, intelligent processes, error detection, automated assembly lines, and robotic collaboration. Computer vision benefits manufacturing processes with high productivity, more safety, and less human error. Challenges for CV in manufacturing include expensive hardware, a lack of skilled professionals, and high-quality data.

With image and video data fueling advancements across various industries, the video and image annotation tool market is witnessing rapid expansion, projected to grow at a compound annual growth rate (CAGR) of 30% between 2023 and 2032. This growth is particularly pronounced in autonomous vehicles, healthcare, and retail sectors, where precise and accurate data annotation is crucial. The increased demand for these tools results from the need to develop robust quality assurance processes, integrate automation for efficiency, collaborate features for team-based annotation, and streamline labeling workflows to produce high-quality training data. However, the extensive choice of annotation tools makes choosing a suitable platform that suits your requirements challenging. There are a plethora of available options, each with varying features, scalability, and pricing models. This article will guide you through this tooling landscape. It highlights five critical questions you must ask before investing in a video annotation tool to ensure it aligns with your project requirements and goals. Key Factors that Hinder Efficient Annotation Project Management A robust video annotation tool helps improve annotation workflows, but selecting an appropriate solution requires you to: Consider the tool’s ability to render videos natively Track objects using advanced algorithms Perform frame-by-frame analysis Doing all those while determining its scalability, quality, integrability, and cost to guide your choice. Below are a few factors that can be potential bottlenecks to your CV project. Native Video Rendering Annotating long-form videos can be challenging if the annotation tool lacks features for rendering videos natively. The operative costs can be prohibitive if you use external tools to render multiple videos, limiting your budget for the annotation project. Object Tracking and Frame-by-Frame Analysis Another obstacle to video annotation is sub-optimal object tracking algorithms that cannot address occlusion, camera shift, and image blur. Traditional tracking algorithms use a detection framework to identify objects within separate video frames. However, detecting and tracking objects frame-by-frame can cause annotation inconsistency and increase data transfer volume. If you are using a cloud platform that charges based on data usage, this will result in inaccurate labels, processing delays, and high storage costs. Scalability Handling large and complex video data is essential for providing a high-quality user experience. However, maintaining quality requires error-free training data with accurate labels to build robust computer vision models that can efficiently process video feeds. Finding a tool that you can quickly scale to rising demands is difficult due to the constantly evolving data landscape. Tools with limited scalability can soon become a bottleneck as you start labeling extensive datasets for training large-scale CV applications. For instance, the pipelines can break as you feed more data. This can result in missed deadlines, deployment delays, and budgetary runs as you hire more annotators to compensate for the tool’s shortcomings. Quality of Annotation Annotation quality directly affects the performance of supervised learning models, which rely heavily on accurately labeled data for training. Consider developing a machine learning model for a surveillance system to detect abnormal behavior and alert relevant authorities to prevent accidents. If the model’s training set included video feeds with erroneous labels, it could not efficiently recognize security threats. This would result in false alarms and missed targets, which would lead to adverse security incidents. Deploying such models in crowded areas can be more detrimental, as the system will not flag suspicious actions in time. Mitigating these problems requires the annotation tool to have quality assurance and collaboration features, which will help human annotators verify labeling accuracy and fix errors proactively. Integrability with Existing Infrastructure Developing robust artificial intelligence (AI) models requires more than the best algorithms and evaluation strategies. Instead, the emphasis should be on an integrated infrastructure that seamlessly handles data collection, storage, preprocessing, and curation. As annotation is a vital element of a data curation pipeline, a tool that quickly integrates with your existing machinery can significantly boost productivity and quality. Businesses that fail to build an integrated system operate multiple disparate systems without synchronization. This results in increased manual effort to organize data assets, which can lead to suboptimal workflows and poor deployment procedures. Cost A data annotation tool that provides flexible pricing options to upgrade or downgrade your plans according to project needs makes financing decisions easier, paving the way for a faster return on investment (ROI). A cost-effective tool helps with executive buy-in as it becomes easier for the management to convince the executive team to undertake innovative projects and continue the development process without budgetary hurdles. Learn how to automate video annotation by reading our guide on video annotation automation. How to Select a Video Annotation Tool Due to the challenges discussed above, choosing a tool that meets your required standards becomes time-consuming and delays the launch of your CV application. So, the following sections explain the primary factors you should consider when investing in a labeling platform. They will help you quickly filter out the desired features to speed up your annotation processes. What are Your Annotation Needs? Understanding the exact annotation requirements should be the first step in selecting a tool, and the following factors must be included: The Type of Computer Vision (CV) Application CV models for applications like autonomous driving and real-time surveillance call for a scalable annotation platform to label large amounts of real-time video feeds. The type of application will also determine what category of annotation is necessary and whether a particular tool offers the required functionality. Critical applications like medical imaging require pixel-level segmentation masks, while bounding boxes will suffice for security surveillance. Automation for Video-specific Complexities Videos with higher frames-per-second (FPS) can take longer to label since annotators must classify objects within each frame. Additionally, videos with higher motion speeds can cause blurred-out frames or motion blur. This is especially true for action recognition CV models, where labeling frequently changing human actions becomes challenging. The solution to these issues is to have tools with automated labeling techniques that use pre-trained models (AI-assisted annotations) to label samples in real time using data pipelines with interpolation algorithms to fix blurry frames. Platform Compatibility and User Interface (UI) A tool compatible with several operating systems and environments can improve integrability and prevent disruptions to annotation projects. Similarly, the tool’s UI must be intuitive so annotators can quickly learn to use the platform, reducing the time required for staff training. Video Format Compatibility For optimal data processing, annotation tools must support multiple video formats, such as MP4, AVI, FLV, etc., and provide features to convert annotations into suitable formats to train CV models quickly. Video Annotation Tool: Must-have Functionalities Based on the above considerations, a video annotation tool must have: Features to natively label video datasets frame-by-frame for advanced object tracking so that minimal downsampling is required. There are basic types of annotations, such as keypoint annotation for pose estimation, 2D bounding boxes, cuboids, polylines, and polygons for labeling objects within a single video frame. Advanced annotation techniques include semantic segmentation, object tracking algorithms, and temporal annotation. Suitable APIs and SDKs can be used to integrate with existing data pipelines programmatically. While these factors are essential for a video annotation tool, it is also advisable to have a manual review process to assess annotation accuracy for high-precision tasks, such as medical imaging, surgical videos, and autonomous navigations. Encord Annotate addresses all the above concerns by offering scalable features and algorithms to handle project complexities, extensive labeling techniques, and automation to speed up the annotation process. How Do You Evaluate Annotation Efficiency? The annotation tool should allow you to compute annotation speed and accuracy through intuitive metrics that reflect actual annotation performance. The list below mentions a few popular metrics for measuring the two factors. Metrics for Measuring Annotation Speed Annotations per hour: Determine the 'annotations per hour' to gauge productivity, contextualizing it with industry norms or project expectations. Frames per minute: Evaluate 'frames per minute' to understand annotator performance in video contexts, considering the video complexity. Time per annotation: Use 'time per annotation' to assess individual annotation task efficiency, adjusting expectations based on the required annotation detail. Metrics for Measuring Annotation Accuracy F1-score: Use the F1-score to balance precision and recall scores, explaining its calculation through Intersection over Union (IoU) in video contexts—IoU determines precision and recall in video frames. Cohen’s Kappa and Fleiss’ Kappa: Use Cohen's Kappa and Fleiss’ Kappa for annotator agreement analysis, providing context for when each is most applicable. Krippendorff’s Alpha: Consider Krippendorff’s alpha for diverse or incomplete datasets, detailing its significance in ensuring consistent annotation quality. Ability to Process Complex Annotation Scenarios Ensure the tool can effectively manage challenges like object occlusion, multiple object tracking, and variable backgrounds. Provide examples to illustrate how these are addressed. Discuss the tool's adaptability to different annotation complexities and how its features facilitate accurate labeling in varied scenarios. Customization and Integrations Customization and integrability with ML models are valuable capabilities that can help you tailor a tool’s annotation features to address use-case-specific needs.  Know if they allow you to use open-source annotation libraries to improve existing functionality. Encord Annotate offers multiple quality metrics to analyze annotation quality and ensures high efficiency that meets current industry standards. How Flexible do you Want the Features to be? While the features mentioned above directly relate to annotation functionality, video annotation software must have other advanced tools to streamline the process for computer vision projects. These include tools for managing ontology, handling long-form video footage, quality control, and AI-based labeling. Ontology Management Ontologies are high-level concepts that specify what and how to label and whether additional information is necessary for model training. Users can define hierarchical structures to relate multiple concepts and create a richer annotated dataset for training CV models. For instance, an ontology for autonomous driving applications specifies that the labeler must annotate a car with 2D bounding boxes and provide information about its model, color, type, etc. These ontologies allow annotators to correctly identify objects of interest in complex videos and include additional information relevant to scene understanding.  Clarifying how users can adapt these ontologies across various project types demonstrates the tool's adaptability to diverse research and industry needs. Features to Manage Long-form Videos Long-form videos pose unique challenges, as annotators must track longer video sequences and manage labels in more frames. Suitable tools that allow you to move back and forth between frames and timelines simplify video analysis. You can easily navigate through the footage to examine objects and scenes. Segmentation: Segmentation is also a valuable feature to look out for, as it allows you to break long videos into smaller segments to create manageable annotation tasks. For instance, automated checks that monitor labels across segments help you identify discrepancies and ensure identical objects have consistent labeling within each segment. Version Control: Finally, version control features let you save and reload previous annotation work, helping you track your progress and synchronize tasks across multiple annotators. Tools that allow annotators to store annotation revision history and tag particular versions help maintain a clear audit trail.  These functionalities improve user experience by reducing fatigue and mitigating errors, as annotators can label long-form videos in separate stages. It also helps with quick recovery in case a particular version becomes corrupt. Customizable Workflows and Performance Monitoring Annotation tools that let you customize workflows and guidelines based on project requirements can improve annotation speed by removing redundancies and building processes that match existing annotators’ expertise. Further, intuitive dashboards that display relevant performance metrics regarding annotation progress and quality can allow management to track issues and make data-driven decisions to boost operational efficiency. Inter-annotator agreement (IAA), annotation speed, and feedback metrics that signify revision cycles are most useful in monitoring annotation efficiency.  For instance, an increasing number of revisions denotes inconsistencies and calls for a root-cause analysis to identify fundamental issues.  AI-assisted Labeling AI-assisted labeling that involves developing models for domain-specific annotation tasks can be costly, as the process requires manual effort to label sufficient samples for pre-training the labeling algorithms. An alternative approach is using techniques like interpolation, semantic and instance segmentation, object tracking, and detection to label video frames without developing a custom model. For example, video annotation tools with object-tracking algorithms can automatically identify objects of interest and fill in the gaps using only a small set of manually labeled data. The method enhances annotation efficiency as annotators do not have to train a separate model from scratch and only label a few items while leaving the rest for AI. Quality Assurance and Access Control Regardless of the level of automation, labeling is error-prone, as it is challenging to annotate each object in all video frames correctly. This limitation requires a tool with quality assurance features, such as feedback cycles, progress trackers, and commenting protocols. These features help human annotators collaborate with experts to identify and fix errors. Efficient access control features also become crucial for managing access across different teams and assigning relevant roles to multiple members within a project. The Encord platform features robust AI-based annotation algorithms, allowing you to integrate custom models, build tailored workflows, and create detailed ontologies to manage long-form videos. What Type of Vendor Are You Looking for? The next vital step in evaluating a tool is assessing different vendors and comparing their annotation services and platforms against standard benchmarks while factoring in upfront and ongoing costs. A straightforward strategy is to list the required features for your annotation project and draw a comparison table to determine which platforms offer these features and at what cost. Here are a few points you should address: Managed Service vs. Standalone Platform: You must see whether you require a managed service or a standalone application. While a managed service frees you from annotating the data in-house, a standalone tool offers more security and transparency in the annotation process. A side-by-side comparison detailing each model's implications on your workflow and data governance practices can guide your decision. Onboarding Costs: Analyze all costs associated with adopting and using the tool, distinguishing between one-time onboarding fees, recurring licensing costs, and any potential hidden fees. Consider creating a multi-year cost projection to understand the total cost of ownership and how it compares to the projected ROI. Ecosystem Strength: A vendor with a robust community and ecosystem offers additional resources to maximize the value of your tool investment, including access to a broader range of insights, support, and potential integrations.  Long-term Suitability: Other relevant factors in evaluating vendors include customer reviews, vendor’s track record in providing regular updates, supporting innovative projects, long-term clients, and customer support quality. Analyzing these will help you assess whether the vendor is a suitable long-run strategic partner who will proactively support your company’s mission and vision.  What is the Standard of Post-purchase Services Investing in a video annotation tool is a long-term strategic action involving repeated interactions with the vendor to ensure a smooth transition process and continuous improvements. Below are a few essential services that vendors must offer post-purchase to provide greater value and meet changing demands as per project requirements. Training Resources: The vendor must provide easy access to relevant training materials, such as detailed documentation, video tutorials, and on-site support, to help users fully utilize the tool’s feature set from the start. Data Security Protocols: While compliance with established security standards, including GDPR, HIPAA, ISO, and SOC, is crucial, the vendor must continuously update its encryption protocols to address the dynamic nature of data and rising privacy concerns. Post-purchase, the vendor must ensure robust security measures by following ethical practices and analyzing sensitive information in your project to implement suitable safeguards to prevent breaches and data misuse. Customer Support: The vendor must offer 24/7 customer support helplines for bug resolution and workflow assistance. Want to know the most crucial features of a video annotation tool? Read our article on the five features of video annotation. Encord complies with HIPAA, FDA, and CE standards, making it an ideal tool for sensitive annotation tasks, especially for medical use cases. Evaluating a Video Annotation Tool: Key Takeaways As CV models permeate multiple domains, such as healthcare, retail, and manufacturing, video annotation tools will be critical determinants of the success of modern CV projects. Below are a few key factors you should consider when evaluating a video annotation platform. Annotation Requirements: The answer will allow you to filter out the desired feature set and scalability demands. Evaluation of Annotation Efficiency: Understanding evaluation methodologies will help you select a tool that offers suitable metrics to assess annotation speed and accuracy. Feature Flexibility: Ontology management, AI-assisted labeling, and options to customize workflows are crucial features that allow you to tailor the tool’s feature set to your requirements. Strategic Vendor Evaluation: Analyzing upfront and ongoing costs helps you determine the total cost of ownership and whether the vendor is a suitable long-term strategic partner. Quality of Post-purchase Services: With the ever-changing data landscape, you need a vendor that constantly updates its security and training protocols to keep pace with ongoing developments.