Computer vision models rely on the detection and identification of objects in photos and videos. It is not a simple process at all. Especially in the case of videos where the object can be in both static and moving state. The size and shape can be of any type and video can be of large length. In short, the process has to be good and foolproof to make sure that mistakes in identification and detection are as few as possible. Video annotation solutions do this. Algorithms are then prepared for various tasks, such as tracking objects via video segments and frames. Video labeling is one aspect of video annotation that needs expertise as it helps the creation of data sets.
One basic difference between image annotation and video annotation is the way of carrying out an annotation. In video annotation, identification and detection make use of frames. Videos get divided into multiple frames. Video labeling helps it turn into a capable dataset for AI and machine learning.