Research on multi-target data association of the infrared fish-eye warning system
Yulong Zhou,
Dan Fang,
Jianchao Li,
Baoquan Zhang,
Minchai Hao,
Jianjun Liu
Affiliations
Yulong Zhou
Hebei Jiaotong Vocational and Technical College, Huai'an East Road No.339, Shijiazhuang, 050035, China; Army Engineering University of PLA, Shijiazhuang Campus, Heping Road No.97, Shijiazhuang, 050003, China
Dan Fang
Army Engineering University of PLA, Shijiazhuang Campus, Heping Road No.97, Shijiazhuang, 050003, China; Corresponding author. Army Engineering University of PLA, Shijiazhuang Campus, Heping Road No.97, Shijiazhuang City, 050003, China.
Jianchao Li
Hebei Vocational University of Industry and Technology, Intelligent Manufacturing Institute, Hongqi Street, Shijiazhuang, 050091, China
Baoquan Zhang
State Grid Hebei Electric Power Co., Ltd UHV Branch, Xinhua Region, Zhongsheng Road No.66, Shijiazhuang, 050071, China
Minchai Hao
Hebei Vocational University of Industry and Technology, Intelligent Manufacturing Institute, Hongqi Street, Shijiazhuang, 050091, China
Jianjun Liu
Hebei Vocational University of Industry and Technology, Intelligent Manufacturing Institute, Hongqi Street, Shijiazhuang, 050091, China
The multi-target data association method is studied in order to realize multi-target tracking in infrared fish-eye warning system. The Neural Joint Probabilistic Data Association (NJPDA) algorithm is analyzed. It is found that the NJPDA algorithm only considers the distance information between the measurement and the target in the data association process, and its tracking accuracy needs to be further improved. Therefore, a new method fused with direction information is proposed based on the NJPDA algorithm. The proposed algorithm defines the concept of direction difference, introduces the direction information of target motion, and modifies the likelihood function by Gaussian weighting method, so as to fuse the direction information of target motion into the calculation of data interconnection probability. Experimental results demonstrate that the tracking success rate of the proposed algorithm is nearly 10 % higher than that of JPDA and NJPDA algorithms and its consuming time meets the real-time requirement of the infrared fish-eye warning system.
