Validating Bounding Box Annotations

What this post added

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.