Automated Bounding Box Annotation Validation

Uber has developed advanced AI and ML-based systems to improve the quality and efficiency of data annotation processes. This includes a focus on validating bounding box annotations in videos to detect and correct errors such as ID swaps and position jumps, which are critical for building object trackers. The system uses a multi-stage pipeline involving feature extraction, ML classification (XGBoost), and post-processing to cluster errors for human review. This initiative aims to reduce annotation costs and time by providing automated outlier detection with confidence scoring, clustering detected errors into actionable groups for human review.