Learnable Online Graph Representations for 3D Multi-Object Tracking

Abstract

Tracking of objects in 3D is a fundamental task in computer vision that finds use in a wide range of applications such as autonomous driving, robotics or augmented reality. 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. 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 show the merit of the proposed approach on the publicly available nuScenes dataset by achieving state-of-the-art performance of 65.6% AMOTA and 58% fewer ID-switches.

Publication
In International Conference on Robotics and Automation, ICRA 2022
Jan-Nico Zäch
Jan-Nico Zäch
PhD Student, ETH Zurich
Martin Danelljan
Martin Danelljan
Researcher

Researcher in Computer Vision and Machine Learning at ETH Zurich