Abstract:

To solve the problems of poor tracking accuracy and sample impoverishment of particle filter algorithm in vehicle tracking application， a multi-feature particle filter vehicle tracking algorithm based on adaptive mutation update strategy and moth-flaming optimization is proposed. Firstly， the tracking stability of the particle filter algorithm in complex scene is improved by combining the complementary characteristics of target texture and color features. Secondly， the adaptive weight is introduced into the spiral update strategy of the moth by changing the update mechanism of the moth-flaming optimization， and then the random mutation strategy is updated alternately to increase the search space of the algorithm， so that the algorithm can search the global optimum more quickly. Finally， the sample particles are layered according to the threshold and the improved moth-flaming optimization is used to optimize the distribution of low weight particles to avoid sample impoverishment. Experiments show that the proposed algorithm can effectively improve the performance of particle filter algorithm in the case of low sample particle number， and can still track the target vehicle stably and accurately in the complex background of shadow， occlusion， scale and angle change.

Key words: target tracking, color feature, texture feature, particle filter, moth?flaming optimization