Image tracking method based on sequential particle swarm optimization

A particle swarm optimization and image tracking technology, applied in the field of visual tracking technology, can solve the problems of particle filter sample degradation and large amount of calculation, and achieve the effect of overcoming the problem of sample degradation and simple evaluation.

Inactive Publication Date: 2011-09-21
INST OF AUTOMATION CHINESE ACAD OF SCI
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AI Technical Summary

Problems solved by technology

Compared with the stochasticity tracking framework, the stochasticity tracking framework is more robust, but computationally expensive and grows exponentially with the target state
In addition, because there is no good mechanism to select the importance distribution, the particle filter has a serious sample degradation problem

Method used

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  • Image tracking method based on sequential particle swarm optimization
  • Image tracking method based on sequential particle swarm optimization
  • Image tracking method based on sequential particle swarm optimization

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Embodiment Construction

[0030] The following is a detailed description of each detail problem involved in the technical solution of the invention.

[0031] The algorithm implemented by the scheme of the present invention is shown in the accompanying drawings. The hardware and the programming language that the method of the present invention is concretely run are not limited, can be finished with any language writing, for this reason other operating modes no longer go into details, only give an example below, adopt a 2.8G Hz central processing unit and 1G The Pentium 4 computer of byte memory has compiled the working program of sequence particle swarm optimization tracking framework with Matlab language, has realized the method of the present invention, and the method of the present invention utilizes to particle random propagation module, particle swarm optimization iterative module, fitness value evaluation module , Particle individual optimal state and group optimal state update module and converge...

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Abstract

The invention relates to an image tracking method based on sequential particle swarm optimization, which comprises the following steps: in a present frame image, randomly spreading an individual optimal state group in the last frame image by utilizing state transition distribution; performing the particle swarm optimization iteration on the particles generated after randomly spreading; evaluating an adaptive value of each particle by utilizing an apparent model of a spatially constrained gaussian mixture; updating the individual optimal state and the group optimal state of the particles according to the evaluating results for the adaptive values; and performing the convergence judgment: if meeting a convergence condition, outputting an observed value corresponding to the particle of a group optimal state as a tracking result of the present frame image, and if not, proceeding with the particle swarm optimization iteration. By using the method, the effective target tracking is realized and the application prospect is excellent.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to visual tracking (Visual tracking) technology. Background technique [0002] The motion tracking of targets in complex scenes is one of the cutting-edge research directions in the field of computer vision in recent years, and it is also one of the difficulties in this field. Especially the target motion analysis in dynamic scenes has been highly valued by many important research institutions in the world, which fully demonstrates its importance. The tracking problem is equivalent to creating a corresponding matching problem based on position, velocity, shape, texture, color and other related features between consecutive image frames. In general, target tracking algorithms must design two key issues: ① Appearance model, that is, how to model the target and update it in real time. Therefore, how to construct a good appearance model plays a crucial role in object recognition. Especi...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/20G06N3/00
Inventor 胡卫明张笑钦罗文寒
Owner INST OF AUTOMATION CHINESE ACAD OF SCI
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