How to annotate video for machine learning: A Step-by-Step workflow
Blog post from Encord
Video annotation for machine learning involves labeling objects, actions, and events across video frames to enable models to learn detection, tracking, and reasoning over time. The process requires defining a taxonomy that aligns with the model architecture, ensuring consistent object tracking through occlusions, and employing both frame-level and sequence-level quality assurance (QA) to maintain data integrity. It is crucial to split datasets at the video level to prevent temporal leakage, which can inflate validation accuracy. The workflow includes steps like preparing raw video footage, selecting appropriate annotation tools, applying keyframe labeling with interpolation, and maintaining consistent object IDs. Effective QA involves frame-level checks for individual label accuracy and sequence-level reviews for temporal consistency. The choice of export format should match the training pipeline's requirements, and annotated video data should be integrated into the ML pipeline without introducing temporal leakage. This meticulous approach ensures that the model learns generalizable patterns rather than memorizing specific video characteristics, ultimately enhancing model performance and reliability.
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