基于混合粒子滤波的多目标跟踪

Abstract

Abstract: For the problem of detecting and tracking a variable number of dim small targets in IR image sequences,a multi-target track-before-detect approach based on the mixture model of probability densities is proposed,and a mixture of t distribution particle filters(MTPF) is developed for the implementation of the proposed approach.In the mixture of particle filters,the existence of each tracked target is detected by using the sequential likelihood ratio test estimated by the output of the component particle filter.New targets are detected by the appearance probabilities in the discrete occupancy grid in the image frame.The proposed approach overcomes the curse of multi-target dimensionality by independently estimating each target state with a separate particle filter,and avoids the exponential increase in the estimation complexity.Importance resampling is carried out in each component particle filter individually.Simulation experiments illustrate that the MTPF algorithm can detect and track the variable number of dim small targets in the IR images.It can also detect the disappearance and appearance of targets simultaneously.

CLC Number:

TN27

LI Shao-jun, ZHU Zhen-fu. Multi-target tracking based on mixtures of particle filtering[J]. Journal of Systems Engineering and Electronics, 2009, 31(8): 1795-1800.

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Multi-target tracking based on mixtures of particle filtering

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