Annotating videos for a deep learning project involves marking frames to provide labeled data for training. Start by splitting the video into frames using tools like OpenCV or FFmpeg. Decide on the type of annotations required: bounding boxes for object detection, keypoints for pose estimation, or segmentation masks for pixel-level tasks.
Use annotation tools like CVAT, VGG Image Annotator, or Labelbox to annotate individual frames. For efficiency, consider using semi-automatic tools or pre-trained models to generate initial annotations, which can then be refined manually.
Maintain annotation consistency across frames, especially for object tracking tasks. Ensure that classes and labels are well-defined to produce a high-quality dataset suitable for training accurate models.