基于HS⁃FCM模糊聚类的快速多目标车辆跟踪算法

doi:10.19562/j.chinasae.qcgc.2021.10.001

Abstract

Abstract:

In order to improve the real-time performance and reliability of multi-target data association in complex traffic scenarios， a fast multi-target vehicle tracking algorithm based on half suppressed fuzzy c-means clustering （HS-FCM） is thus proposed in this article. Firstly， the multi-target vehicle-tracking problem is described mathematically， and the spatial mapping relationship between the camera pixel coordinate system and the world coordinate system is established. Secondly， the fuzzy clustering approach based on fuzzy theory is employed to solve the plot-track association problem. The probability of a feasible joint event in the joint probability data association （JPDA） algorithm is indirectly calculated by solving the fuzzy membership function defined by the distance between a sample and its cluster center. The multi-objective state is filtered and estimated by the probability weighted fusion method. Thirdly， in the dense vehicle environment the measurement update is suppressed by adjusting Kalman gain reasonably to solve the problem of short-term target losing. The real vehicle test and simulation results validate the feasibility and effectiveness of the proposed fast multi-vehicle tracking algorithm.

Key words: intelligent vehicle, data fusion, object of interest, state estimation, fuzzy C?means

Junhui Zhang,Zongjie Fu,Xiaoman Guo,Qing Li,Dapeng Chen,Ye Zhao. A Fast Multiple Maneuvering Vehicle Tracking Algorithm Based on Half Suppressed Fuzzy C⁃means Clustering[J].Automotive Engineering, 2021, 43(10): 1419-1426.

Figures/Tables 12

References 19

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传感器	参数	数值
毫米波雷达	型号	大陆ARS 408-21
	载波频率	77 GHz
	最大测距	250 m（长距模式）
	最大测距	70 m（近距模式）
	量测精度	±0.4 m（长距模式）
	量测精度	±0.1 m（近距模式）
	水平角	±9°（长距模式）
	水平角	±45°（近距模式）
	速度范围	-400 km/h~200 km/h
	通信方式	CAN
	扫描周期	约60 ms
相机	型号	Logitech C310
	分辨率	1280×720
	焦距	29.5 mm
	帧率	30 fps

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A Fast Multiple Maneuvering Vehicle Tracking Algorithm Based on Half Suppressed Fuzzy C⁃means Clustering

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