Video annotation is based on the concept of image annotation. For video annotation, features are manually labelled on every video frame (image) to train a machine learning model for video detection. Hence, the dataset for a video detection model is composed of images for the individual video frames.

It is the process of labelling or tagging video clips which are used for training computer vision models to detect or identify objects. Unlike image annotation, video annotation involves annotating objects on a frame-by-frame basis to make them recognizable for machine learning models.

High-quality video annotation generates ground truth datasets for optimal machine learning functionality. There are numerous deep learning applications for video annotation across industries including self-driving cars, medical AI, and geospatial technology.