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Target tracking on the complex background in the infrared image sequence is hot
research field. It provides the important basis in some fields such as video monitoring, precision,
and video compression human-computer interaction. As a typical algorithms in the target tracking
framework based on filtering and data connection, the particle filter with non-parameter
estimation characteristic have ability to deal with nonlinear and non-Gaussian problems so it were
widely used. There are various forms of density in the particle filter algorithm to make it valid
when target occlusion occurred or recover tracking back from failure in track procedure, but in
order to capture the change of the state space, it need a certain amount of particles to ensure
samples is enough, and this number will increase in accompany with dimension and increase
exponentially, this led to the increased amount of calculation is presented. In this paper particle
filter algorithm and the Mean shift will be combined. Aiming at deficiencies of the classic mean
shift Tracking algorithm easily trapped into local minima and Unable to get global optimal under
the complex background. From these two perspectives that "adaptive multiple information fusion"
and "with particle filter framework combining", we expand the classic Mean Shift tracking
framework .Based on the previous perspective, we proposed an improved Mean Shift infrared
target tracking algorithm based on multiple information fusion. In the analysis of the infrared
characteristics of target basis, Algorithm firstly extracted target gray and edge character and
Proposed to guide the above two characteristics by the moving of the target information thus we
can get new sports guide grayscale characteristics and motion guide border feature. Then proposes
a new adaptive fusion mechanism, used these two new information adaptive to integrate into the
Mean Shift tracking framework. Finally we designed a kind of automatic target model updating strategy to further improve tracking performance. Experimental results show that this algorithm
can compensate shortcoming of the particle filter has too much computation, and can effectively
overcome the fault that mean shift is easy to fall into local extreme value instead of global
maximum value .Last because of the gray and fusion target motion information, this approach also
inhibit interference from the background, ultimately improve the stability and the real-time of the
target track.
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