Learnable Online Graph Representations for 3D Multi-Object Tracking

Autonomous systems that operate in dynamic environments require robust object tracking in 3D as one of their key components. Most recent approaches for 3D multi-object tracking (MOT) from LIDAR use object dynamics together with a set of handcrafted features to match detections of objects across multiple frames. However, manually designing such features and heuristics is cumbersome and often leads to suboptimal performance. In this work, we instead strive towards a unified and learning based approach to the 3D MOT problem. We design a graph structure to jointly process detection and track states in an online manner. To this end, we employ a Neural Message Passing network for data association that is fully trainable. Our approach provides a natural way for track initialization and handling of false positive detections, while significantly improving track stability. We demonstrate the merit of the proposed approach in the nuScenes tracking challenge 2021 with a state-of-the-art performance of 65.6% AMOTA with 58% fewer ID-switches, resulting in the best LIDAR only submission and an overall second place.