5 Questions to Ask When Evaluating a Video Annotation Tool

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.

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.

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.

1. Annotation Requirements: The answer will allow you to filter out the desired feature set and scalability demands.
2. Evaluation of Annotation Efficiency: Understanding evaluation methodologies will help you select a tool that offers suitable metrics to assess annotation speed and accuracy.
3. 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.
4. 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.
5. 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.