Annotating required parameters of a video to capture its each objects whether still or moving and adding metadata and labels to make them more precise and detectable for machine leaning is called video annotation. These metadata and labels could range from a bounding box to full semantic segmentation of frame/image making the object detectable across multiple frames.

Track Human Activity and Pose Estimation: Accurately labelled facial expressions and human poses can make recognition of human activities under various situation easy for computer vision which helps to create better operating AI based machine learning model.

Object Tracking for Self-driving Vehicles: Self-driving vehicles can detect the objects like other vehicles, lanes, street lights, sign boards, traffic signals, traffic on the road, humans, and other still or moving objects on the street using the annotated videos. A good annotation would allow computer vision to build best model to develop a fully functional, safe and reliable self-driving vehicle.

Video Annotation for Computer Vision: We can annotate the any video using the available advance tools and techniques to create data with highest quality for precise results to build the computer vision.