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Labeling and annotating images is easy. Video annotation is not. Too many platforms focus on image annotation, throwing in video as an additional suite of features rather than implementing video-native tools for annotators.
In this post, we outline the 5 features you need to maximize video annotation ROI and efficiencies so you can choose the right video annotation tool for your needs.
Video annotation is not the same as image annotation. You need a completely different — specialist, video-centric — a suite of tools and features to handle videos.
Otherwise, data and video analyst teams are juggling multiple annotation platforms (which is something we see more often than you’d imagine) to achieve their objectives.
As a leader or manager within an organization that needs a video annotation and labeling solution, you must ensure that the platform can effectively handle the specificities of video and image annotation.
For example, within a large video — with a long runtime — you need to ensure the correct coordinates of objects that move from one frame to the next are aligned with the frame and timestamp the object first appeared.
For several reasons, this doesn’t always happen with other tools, forcing companies to discard months’ worth of incorrectly labeled data. Let’s review the five most important features you need when considering which video annotation tool/platform to use.
Video annotation comes with dozens of challenges, such as variable frame rates, ghost frames, frame synchronization issues, and numerous others. To avoid these issues and ensure you don’t lose days of labeling activity, there are two things your video annotation platform needs:
Effective pre-processing solves these challenges, ensuring a video is displayed properly and ready for annotation. Pre-processing means you avoid needing to re-label everything if there’s an issue with the video (e.g., sync frame issues, video not displayed properly, annotations are not matched with the proper frames, etc.), saving your annotation team countless hours and a lot of budgets at the start of a project.
An easy-to-use video annotation and labeling interface ensures that annotators are productive. Video labeling and annotation shouldn’t take months, especially when annotating long videos. With this in mind, here are the key features you need to look out for to ensure your chosen annotation tool is easy to use:
Navigation features in the video annotation section of Encord
Another important feature of a great video annotation tool is the ability to classify frames and events. This gives you additional data for your model to work from - whether it was nighttime in the video or what the labeled object was doing at the time.
Dynamic classifications are often called action or “event-based” classifications. The clue is in the name - they tell you what the object is doing - whether the car that you’re tracking is turning from left to right over a specific number of frames; hence these classifications are dynamic. It depends on what’s going on in the video and the granular level of detail you need to label. Dynamic or event-based classifications are a powerful feature that the best video annotation platforms come with, and you can use them regardless of the annotation type used to originally label the object in motion.
Frame Classifications are different from specific object classifications. Instead of labeling or classifying an object, you use an annotation tool to organize a specific frame within a video. Hotkeys and video labeling menus can make it simple to select the start and end of a frame and then give that frame a label while annotating. A frame classification is used to highlight something happening in the frame itself - whether it is day or night or raining or sunny, for example.
Annotation is a time-consuming, manual, data-intensive task. Especially when videos are long, complicated, or there are hundreds of videos to annotate. A solution is to automate video annotations.
Automation leverages the skills of your annotation teams. It saves time and money while increasing the efficiency and the quality of the annotation work.
For example, if an annotator has drawn a label boundary around a specific object — e.g., a cellular cluster being analyzed — the goal of auto-object segmentation is to tighten the edges so it fits more closely around the image in question. Algorithms can also track this image throughout the video automatically.
Example of automated labeling using interpolation in Encord
Large annotation teams are difficult to manage. Whether you’re a Head of Machine Learning or Data Operations leader, you’ve got to juggle team management, budgets, operational timelines, and project outputs.
Project leaders need visibility on what’s going on, being processed, and being analyzed. You need a clear understanding of the state of the project in real-time, giving you the ability to react fast if anything changes.
When big-budget and long-timescale annotation projects are underway, it’s often useful to leverage external annotation teams to implement labor-intensive aspects of the project.
But working with external providers creates the need for advanced team and project management features, such as:
User management in Encord
And there we go, the 5 features every video annotation tool needs.
At Encord, our active learning platform for computer vision is used by a wide range of sectors - including healthcare, manufacturing, utilities, and smart cities - to annotate videos and accelerate their computer vision model development.
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