Video annotation is the process of labeling objects and events across the frames of a video so a model can learn from motion over time. It extends image annotation with temporal continuity, tracking the same object frame to frame rather than labeling each frame in isolation.
Video annotation applies labels, boxes, masks, keypoints, classes, or events, to footage frame by frame, but its defining feature is time. The same object must keep a consistent identity as it moves, so video annotation is less about labeling thousands of independent images and more about maintaining continuity: tracking objects, marking when actions start and stop, and handling objects that leave and re-enter the frame.
Because video has so many frames, it leans heavily on aids like keyframing, interpolation, and model-assisted propagation to keep the effort manageable.
Key takeaways
Video annotation adds time to image annotation, so the same object is tracked across frames.
It covers objects (tracked boxes or masks), events (when something happens), and actions.
Frame counts make it expensive, so keyframes, interpolation, and propagation are essential.
What video annotation provides
Common types of video annotation.
Common types of video annotation.
Type
What it captures
Object tracking
A consistent identity per object across frames
Event and temporal annotation
Marking when an action or state occurs
Segmentation and keypoints over time
Masks or skeletons propagated across frames
Keyframe plus interpolation
Label sparse frames and fill the rest automatically
How it works
Annotators label keyframes and propagate or interpolate between them, then fix where identity or boundaries drift. In FiftyOne, video datasets carry frame-level labels and per-object IDs, so you can play clips, inspect tracks, and compare predictions to ground truth across time.
Why it matters
Video is where physical AI lives, driving, robotics, and surveillance, and it is where annotation cost explodes, because a one-minute clip at 30 fps is 1,800 frames. The cost is dominated by a small fraction of "hard" frames, occlusions, crossings, and fast motion, while long easy stretches interpolate almost for free. So the leverage is not labeling every frame evenly, it is spending human effort on the moments where tracking breaks, which is exactly where SAM 2-style propagation plus targeted correction pays off most.
Frequently asked questions
What is the difference between image and video annotation?
Image annotation labels independent images. Video annotation tracks the same objects across frames over time.
How do you make video annotation efficient?
Label keyframes and use interpolation and model-assisted propagation for the frames between.
What can you annotate in video?
Tracked objects, segmentation, keypoints, and temporal events or actions.