Integrated Detection and Tracking Framework for 3D Multi-Object Tracking in Vehicle-Infrastructure Cooperation

Vehicle-infrastructure cooperative perception has emerged as a promising approach to enhance 3D multi-object tracking by leveraging complementary data from vehicle and infrastructure sensors. However, existing methods face significant challenges, including difficulty in handling occlusions, suboptimal identity association, and inefficiencies in trajectory management, limiting their performance in real-world scenarios. In this paper, we propose a novel vehicle-infrastructure cooperative 3D multi-object tracking framework that addresses these challenges through three key innovations. First, an integrated detection-tracking framework jointly optimizes object detection and tracking, enhancing temporal consistency and reducing errors caused by separately handling the two tasks. Second, the XIOU identity association metric leverages 3D spatial and geometric relation-ships, ensuring robust object matching even under occlusions. Third, a four-stage cascade matching (FSCM) strategy adaptively manages trajectories by leveraging detection and prediction confidences, enabling accurate tracking in complex environments. Evaluated on the V2X-Seq dataset, our method achieves a MOTA of 57.23 and a MOTP of 74.64, significantly reducing identity switches while ensuring low bandwidth consumption and reliable tracking, highlighting its effectiveness and suitability for real-world deployment.

@article{Hu2024,
  title     = {Integrated Detection and Tracking Framework for 3D Multi-Object Tracking in Vehicle-Infrastructure Cooperation},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {11},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Tao Hu and Ping Wang and Xinhong Wang},
  doi       = {10.14569/IJACSA.2024.01511121},
  url       = {https://doi.org/10.14569/IJACSA.2024.01511121}
}