Video annotation is becoming increasingly popular in the professional sector, allowing users to add commentary, tags, and descriptions while viewing videos. This facilitates the development of more structured and organized content which can then be used for a variety of purposes such as analysis, feedback, review or summarization. Video annotation tools provide powerful features that help professionals make better use of multimedia content which is especially useful when dealing with long videos and learning materials. An emerging use case is allowing teachers to annotate student-created videos in order to provide feedback and assess understanding. With advances in technology, video annotations are becoming easier to create and incorporate into digital projects. By providing an interactive element to multimedia content, video annotation tools can help professionals collaborate more efficiently on projects whilst also streamlining data acquisition and management processes for enhanced data analysis.
In case of Video Annotation we do exactly the same by they are frame-by-frame approach. We consider each frame for the marking and the key-frames annotated with or without object tracking. So one can easily know the motion of the object and track the information tagged on the same. This is a very interesting area in AI to better security and people management with more advanced technologies covering face recognition and behaviour of a person, their facial expressions, objects, animals etc. Video annotation in semantic analysis has found its applications in social media monitoring and gets an insight of human emotions to produce data analysis and predictability of behaviour.