OATracker: Object-aware anti-occlusion 3D multiobject tracking for autonomous driving

Abstract

Efficient and accurate 3D multiobject tracking (MOT) is essential for autonomous driving. However, existing methods still struggle with multiobject occlusion, in which interruptions or even loss of crucial tracked objects is likely. Therefore, a more intelligent object-aware anti-occlusion 3D MOT for autonomous driving, named OATracker, is developed in this paper. First, an updated 3D cascaded detection (3DCD) is adopted as the detection backbone, with incorporation of an updatable component based on the OpenPCDet framework to increase the longevity. Second, a reliable object state-constrained affinity (OSA) metric is designed that adaptively combines 3D geometric and motion metrics to dynamically measure objects’ size and distance in both spatial and temporal dimensions. Third, an object-aware trajectory management (OTM) module is developed for trajectory management and status updating. Specifically, the object identity-aware adaptive tracking life manager (ATLM) allows the tracker to adaptively manage the trajectories of captured objects through a confidence metric for each objects, and the ghost-aware trajectory postoptimizer (GTP) associates the historical and detected motion states, further overcoming id-switch and fragmentation problems. Finally, experiments on the KITTI-Car tracking benchmark and practical autonomous driving car application platform show that the OATracker can achieve competitive and robust tracking performance even in occlusion conditions.