To annotate videos for a deep learning project, start by defining the annotation task and selecting tools that match your needs. Video annotation involves labeling objects, actions, or events frame-by-frame or across sequences. For object detection or tracking, you might use bounding boxes or polygons, while action recognition could require labeling temporal segments. Tools like CVAT (Computer Vision Annotation Tool), Label Studio, or VIA (VGG Image Annotator) are popular open-source options. Commercial tools like Scale AI or Amazon SageMaker Ground Truth offer advanced features but may require a budget. Choose a tool that supports your annotation format (e.g., COCO, YOLO, or Pascal VOC) and integrates with your training pipeline.
Next, structure your workflow for efficiency and consistency. Split videos into smaller clips or frames to simplify annotation. For example, use FFmpeg to extract frames at a specific rate (ffmpeg -i input.mp4 -vf fps=1 frame_%04d.jpg). If labeling temporal actions, mark start and end times in tools like CVAT. For object tracking, use interpolation features to propagate labels across frames, reducing manual work. Establish clear guidelines: define class names, handle occlusions, and decide whether to annotate every frame or keyframes. For instance, annotating every 5th frame and interpolating in between can save time while maintaining accuracy. Use scripts to validate annotations (e.g., checking for missing labels or coordinate boundaries) to avoid training errors later.
Finally, manage and export annotations for training. Store annotations in a structured directory with matching frame filenames and labels. For example, save bounding boxes as JSON files with {frame_id: 123, class: "car", xmin: 100, ymin: 200, ...}. Convert annotations into formats compatible with your framework—PyTorch might require COCO-style JSON, while TensorFlow could use TFRecords. If using custom models, write a preprocessing script to load annotations (e.g., with open('labels.json') as f: data = json.load(f)). Version control annotations with Git LFS or DVC to track changes. Always back up raw videos and annotations separately to prevent data loss. For quality assurance, review a subset of annotations manually and measure inter-annotator agreement if multiple labelers are involved.