Non-ellipsoidal extended object tracking using PMBM filter

Youan Zhang; Peng Li; Wenjie Zhang; Jie Wang; Kejie Zhang

doi:10.1117/12.2684191

1 August 2023 Non-ellipsoidal extended object tracking using PMBM filter

Youan Zhang, Peng Li, Wenjie Zhang, Jie Wang, Kejie Zhang

Proceedings Volume 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023); 127542N (2023) https://doi.org/10.1117/12.2684191
Event: 2023 3rd International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 2023, Hangzhou, China

Abstract

The Poisson multi-Bernoulli mixture (PMBM) filter is an effective tracking framework for tracking multiple extended objects. However, methods based on this framework typically assume that the object's shape is an ellipse, which cannot adequately describe the object's shape. When two objects are spatially close, problems such as difficulty correctly distinguishing the object trajectory and insufficient utilization of the object shape's feature information arise. To address the aforementioned issues, this paper describes the shape of the object in greater detail using the multi-ellipse model. On this premise, the extended object shape will be divided into multiple Gaussian inverse Wishart components, and the likelihood will be calculated. Furthermore, a new partitioning method is proposed to divide the measurements into several most linearly dependent components, which aids in estimating the object shape composed of multiple ellipses. The simulation results show that the new algorithm outperforms the original PMBM algorithm in terms of accuracy.

Citation Download Citation

Youan Zhang, Peng Li, Wenjie Zhang, Jie Wang, and Kejie Zhang "Non-ellipsoidal extended object tracking using PMBM filter", Proc. SPIE 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 127542N (1 August 2023); https://doi.org/10.1117/12.2684191

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KEYWORDS

Tunable filters

Detection and tracking algorithms

Electronic filtering

Kinematics

Mathematical modeling

Matrices

Mixtures

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