[1]LU Jun,WANG Xudong,JI Guangyu,et al.Point cloud multitarget tracking algorithm based on the constant turn rate and acceleration model[J].CAAI Transactions on Intelligent Systems,2025,20(6):1328-1338.[doi:10.11992/tis.202503034]
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Point cloud multitarget tracking algorithm based on the constant turn rate and acceleration model

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 6 Page number: 1328-1338 Column: 学术论文—机器学习 Public date: 2025-11-05
Title:
Point cloud multitarget tracking algorithm based on the constant turn rate and acceleration model
Author(s):
LU Jun;  WANG Xudong;  JI Guangyu;  LI Yang
College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
Keywords:
point cloud; multitarget tracking; deep learning; autonomous driving; confidence-based two-stage matching; nonlinear dynamics; data association; Hungarian algorithm
CLC:
TP391
DOI:
10.11992/tis.202503034
Abstract:
To address the suboptimal performance of simple motion models in the context of multitarget tracking within intricate driving environments, this paper proposes a point cloud-based multitarget tracking method that utilizes the constant turn rate and acceleration (CTRA) model. By incorporating angular velocity into the motion model to describe object trajectories, the method improves tracking accuracy during turning maneuvers. Furthermore, the velocity information provided by the detection algorithm is leveraged to correct object speeds during trajectory updates, enhancing robustness under sudden velocity changes. Moreover, a confidence-based two-stage matching strategy is employed to mitigate the influence of low-confidence detections on tracking results. The proposed 3D object detection and tracking algorithm is evaluated on the nuScenes validation set, and ablation studies are conducted to verify the effectiveness of each module. Experimental results demonstrate that the CTRA-based point cloud multitarget tracking algorithm exhibits superior performance in terms of tracking accuracy, significantly enhances performance in turning and abrupt velocity change scenarios, and substantially reduces identity switches during tracking when compared to simple motion models.
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