摘要
Target recognition and tracking is an important research filed in the surveillance industry.Traditional target recognition and tracking is to track moving objects, however, for the detected moving objects the specific content can not be determined.In this paper, a multi-target vehicle recognition and tracking algorithm based on YOLO v5 network architecture is proposed.The specific content of moving objects are identified by the network architecture, furthermore, the simulated annealing chaotic mechanism is embedded in particle swarm optimization-Gauss particle filter algorithm.The proposed simulated annealing chaotic particle swarm optimization-Gauss particle filter algorithm(SA-CPSO-GPF) is used to track moving objects.The experiment shows that the algorithm has a good tracking effect for the vehicle in the monitoring range.The root mean square error(RMSE), running time and accuracy of the proposed method are superior to traditional methods.The proposed algorithm has very good application value.
作者
王伟峰
YANG Bo
LIU Hanfei
QIN Xuebin
WANG Weifeng;YANG Bo;LIU Hanfei;QIN Xuebin(School of Safety Science and Engineering,Xi’an University of Science and Technology,Xi’an 710054,P.R.China;School of Electrical and Control Engineering,Xi’an University of Science and Technology,Xi’an 710054,P.R.China)
基金
Supported by the National Key R&D Plan of China (2021YFE0105000)
the National Natural Science Foundation of China (52074213)
Shaanxi Key R&D Plan Project (2021SF-472)
Yulin Science and Technology Plan Project (CXY-2020-036)。
