MOTCues: 3D Multi-Object Tracking with Birth Prior and Shape Description Informed by Point Cloud Cues

Abstract

Reliable multi-object tracking (MOT) is essential for autonomous systems but remains challenging due to ambiguous object characteristics such as birth, death, and motion models, as well as detector errors including false detections and missed objects. Random finite set (RFS) theory provides a rigorous mathematical foundation that enables the formulation of fundamental uncertainties in object estimation under the Bayesian framework. We propose MOTCues, a MOT algorithm built on the RFS-based Poisson multi-Bernoulli filter, which integrates informative components derived from point cloud cues into the estimator as a tailored formulation. The object birth intensity function is modeled with a Gaussian mixture distribution for effective initialization of new-born objects, while object shape information is captured by constructing bounding box-centric descriptors to enhance hypothesis management. Evaluations on the KITTI dataset and the nuScenes benchmark demonstrate that integrating point cloud cues improves tracking performance by reducing ID switches, achieving superior results compared to baseline model-based trackers in real-world object tracking scenarios.