How to annotate video for ADAS and autonomous driving: A Technical Guide

Video annotation for Advanced Driver Assistance Systems (ADAS) and autonomous vehicles is a complex process that involves labeling driving footage over time to help models track objects, estimate distances, and understand scene context. Unlike image annotation, which labels each frame independently, video annotation focuses on maintaining the identity and motion of objects across frames, which is crucial for models to predict future positions. This requires a persistent ID system and a keyframe interpolation workflow to manage the high volume of frames while ensuring data integrity. The guide also discusses the importance of sensor fusion, aligning data from cameras, LiDAR, and radar to maintain consistent object identity across different sensors. Annotation requirements vary with ADAS levels, with higher levels needing broader coverage for scenarios without human intervention. Environmental conditions and regional driving variations are also important considerations, as they affect model performance if not adequately represented in training data. Quality assurance processes must focus on temporal consistency, ensuring that object labels evolve plausibly over time, to catch errors that a frame-level review might miss. Encord's platform is highlighted for its native video and 3D support, which enhances tracking stability and sensor fusion depth, crucial for reliable annotation in ADAS and AV applications.

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