Video annotation is the process of labeling or tagging specific objects, events, or frames within a video to create structured data. This technique is essential in fields like computer vision, autonomous driving, and video analytics, enabling machines to understand and interpret video content for training machine learning models or performing automated analysis.

What Is Video Annotation?
Video annotation involves identifying and marking key elements in a video, such as objects, actions, or regions of interest, across individual frames or entire sequences. This process may include techniques like bounding boxes, polygons, semantic segmentation, or keypoint tracking. Unlike static image annotation, video annotation considers temporal continuity, ensuring that annotations are consistent and accurate across consecutive frames. This makes it particularly useful for applications that require tracking movement or changes over time.

Tools for Video Annotation

To perform video annotation effectively, we use vector annotation tools:

Annotation Output

When working with annotations, it is essential to understand how they are represented in JSON format. While tasks and their annotations are visible in the workspace, the corresponding JSON structure provides a deeper insight into how the data is organized and stored.

View the JSON recipe Here