A Multi-Feature Particle Filter Vehicle Tracking Algorithm Based on Adaptive Interpolation Moth-Flame Optimization

被引：0
|
作者：
Huang H. [1 ,2 ]
Wu K. [1 ,2 ]
Li X. [1 ]
Wang J. [2 ,3 ]
Wang H. [1 ]
Ru F. [1 ,2 ]
机构：
[1] School of Electronics and Control Engineering, Chang'an University, Xi'an
[2] Xi'an Key Laboratory of Intelligent Expressway Information Fusion and Control, Chang'an University, Xi'an
[3] School of Information Science and Technology, Northwest University, Xi'an
来源：
Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University | 2022年 / 56卷 / 02期
关键词：
Moth-flame; Multi-feature fusion; Particle filter; Vehicle tracking;
D O I：
10.16183/j.cnki.jsjtu.2021.037
中图分类号：
学科分类号：
摘要：
In order to solve the problem of low accuracy and the poor global searching ability of the moth-flame optimization algorithm, an improved adaptive interpolation moth-flame optimization algorithm is proposed, which is embedded into multi-feature particle filter to optimize. Besides, a multi-feature particle filter vehicle tracking algorithm based on adaptive interpolation moth-flame optimization is constructed. First, adaptive weights are added to the moths' position updating mechanism to improve the global searching ability of the proposed algorithm. Next, the adaptive interpolation moth-flame optimization algorithm is used to optimize the sampling process. Then, in combination with the multi-feature adaptive fusion particle filter vehicle tracking algorithm, the particle distribution according to the latest observation information is continuously adjusted, so that the particles in the low weight layer can move to the area with higher weight to enhance the particle quality and avoid sample degradation. The experimental results show that the proposed algorithm can effectively reduce the number of sample particles required for state prediction, improve the tracking performance of the algorithm, and track the target vehicle accurately and stably under the interferences of occlusion, illumination, attitude, and scale changes. © 2022, Editorial Board of Journal of Shanghai Jiao Tong University. All right reserved.
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页码：143 / 155
页数：12
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