> For the complete documentation index, see [llms.txt](https://labelfi-1.gitbook.io/labelfi/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://labelfi-1.gitbook.io/labelfi/basics/labelfi-data-labeling-services/4.2-video-annotation.md).

# 4.2 Video Annotation

Video annotation is more complex than image annotation due to the temporal aspect of videos, requiring the tracking of objects and events across multiple frames. Labelfi supports the following key methods:

* Frame-by-Frame Annotation: Annotating individual frames of a video, either manually or automatically, to create precise tracking of objects across time.
* Object Tracking: Continuously tracking the position and movement of an object throughout a video, which is essential for applications such as surveillance, autonomous driving, and sports analysis \[12].
* Event and Action Recognition: Labeling actions or events taking place in a video, enabling the training of models for behavior or activity recognition in real-time settings \[26].

Labelfi leverages machine learning techniques for automating parts of the video annotation process, while human annotators validate and refine the results to maintain high accuracy.