Probability hypothesis density filter for adjacent multi-target tracking

2021 ◽  
Author(s):  
Mian Wu ◽  
Daikun Zheng ◽  
Junquan Yuan ◽  
Alei Chen ◽  
Chang Zhou ◽  
...  
Keyword(s):  
Target Tracking ◽  
Probability Hypothesis Density ◽  
Probability Hypothesis Density Filter ◽  
Multi Target Tracking ◽  
Density Filter

scholarly journals Innovative unscented transform–based particle cardinalized probability hypothesis density filter for multi-target tracking

2019 ◽  
Vol 52 (9-10) ◽  
pp. 1567-1578
Author(s):  
Bo Li ◽  
Huawei Yi ◽  
Xiaohui Li
Keyword(s):  
Target Tracking ◽  
Particle Method ◽  
Surveillance Systems ◽  
Tracking Accuracy ◽  
Proposal Distribution ◽  
Probability Hypothesis Density ◽  
Unscented Transform ◽  
Probability Hypothesis Density Filter ◽  
Multi Target Tracking ◽  
Density Filter

Multi-target tracking is widely applied in video surveillance systems. As we know, although the standard particle cardinalized probability hypothesis density filter can estimate state of targets, it is difficult to define the proposal distribution function in prediction stage. Since the robust particles cannot be effectively drawn, the actual tracking accuracy should be enhanced. In this paper, an innovative unscented transform–based particle cardinalized probability hypothesis density filter is derived. Considering the different state spaces, we use the auxiliary particle method and then draw robust particles from the modified distributions in order to estimate the position of targets. Simultaneously, we present the recursion of the optimized Kalman gain to improve the general unscented transform for the velocity estimates. Using the track label, we further integrate them in the framework of the jump Markov model. The simulation results show that the proposed filter has advances in the multi-target tracking scenes. Moreover, the experiments indicate that the filter can track mobile targets with satisfactory results.

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