  Online 3D multi-object tracking (MOT) has recently received significant
research interests due to the expanding demand of 3D perception in advanced
driver assistance systems (ADAS) and autonomous driving (AD). Among the
existing 3D MOT frameworks for ADAS and AD, conventional point object tracking
(POT) framework using the tracking-by-detection (TBD) strategy has been well
studied and accepted for LiDAR and 4D imaging radar point clouds. In contrast,
extended object tracking (EOT), another important framework which accepts the
joint-detection-and-tracking (JDT) strategy, has rarely been explored for
online 3D MOT applications. This paper provides the first systematic
investigation of the EOT framework for online 3D MOT in real-world ADAS and AD
scenarios. Specifically, the widely accepted TBD-POT framework, the recently
investigated JDT-EOT framework, and our proposed TBD-EOT framework are compared
via extensive evaluations on two open source 4D imaging radar datasets:
View-of-Delft and TJ4DRadSet. Experiment results demonstrate that the
conventional TBD-POT framework remains preferable for online 3D MOT with high
tracking performance and low computational complexity, while the proposed
TBD-EOT framework has the potential to outperform it in certain situations.
However, the results also show that the JDT-EOT framework encounters multiple
problems and performs inadequately in evaluation scenarios. After analyzing the
causes of these phenomena based on various evaluation metrics and
visualizations, we provide possible guidelines to improve the performance of
these MOT frameworks on real-world data. These provide the first benchmark and
important insights for the future development of 4D imaging radar-based online
3D MOT algorithms.
