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Automated Bounding Box Annotation Validation

Automated Bounding Box Annotation Validation

1
posts
2026

Uber is developing 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 annotatio

2026

Validating Bounding Box Annotations

4/23/2026

This post details the architecture and dataset generation for Uber's ML-based bounding box validation system. It introduces a four-stage pipeline: input processing, feature extraction (visual, motion, coordinate), ML classification using XGBoost, and post-processing/clustering. The dataset is synthetically generated with jump and ID swap perturbations. Features are categorized and an 11-frame sliding window is used for temporal context. The XGBoost model is chosen for its ability to handle heterogeneous features, fast inference, and consistent performance.