Poor video annotation creates noisy annotated data. Small labeling errors across video frames break temporal context and tracking consistency. Studies show that model accuracy can drop from 95% to nearly 74% when trained on low-quality annotations rather than high-quality annotated data.
Video annotation for computer vision is the process of labeling objects, actions, or regions in video frames to create ground-truth data for computer vision models. It involves drawing bounding boxes, polygons, segmentation masks, or keypoints on objects of interest in each frame.
Video annotation is the process of adding labels and metadata to video frames (or time segments) so ML models can learn to detect, track, and understand objects, actions, and events over time.
