An Adaptive Particle Swarm Optimization Particle Filter Tracking Method

Abstract

The invention provides a self-adaptive particle swarm optimization (PSO)-based particle filter tracking method. The method comprises the following steps: S1, selecting a candidate target, and setting the frame of the selected candidate target as a first frame; S2, creating a reference color histogram template for the candidate target, and measuring the similarity between the color histogram of the candidate target and the reference color histogram template according to the bhattacharyya coefficients; S3, randomly distributing M particles in the periphery of the t-1 frame target in the t frame according to the Gauss, and adjusting the parameters of PSO algorithm according to the movement state, wherein t is more than 1; S4, searching the M particle according to the PSO algorithm, and then performing estimation calculation to obtain the position of the target. With the adoption of the self-adaptive PSO-based particle filter packing method, video targets can be tracked well in a complex conditions, including a moving target, a static target and moving-stopping-moving target and the like, and stable tracking of the complex movement targets above can also be realized well.

Description

technical field [0001] The invention relates to the technical field of computer vision tracking, in particular to the field of visual target tracking methods for adaptively adjusting particle swarms according to target motion states, specifically an adaptive particle swarm optimization particle filter tracking method. Background technique [0002] In the field of computer vision, robust and accurate real-time visual tracking is an urgent and challenging task. In recent years, there have been many breakthroughs in the tracking of specific objects, such as pedestrians and face tracking. In the paper "Structure preserving object tracking" published by Zhang, Lu et al. on "IEEE Conference on Computer Vision and Pattern Recognition", it is mentioned that the precise real-time tracking of general objects is still extremely difficult. The main reasons are as follows: 1) About this There is too little information available for the target; 2) The obtained information is ambiguous, a...

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Problems solved by technology

[0006] In view of the shortcomings of the prior art described above, the purpose of the present invention is to provide an adaptive particle swarm optimization particle filter tracking method, which is used to solve the problem of poor tracking effect of visual targets with complex motion state changes in the prior art

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