Dynamic Determination of Histogram Resolution for Object Tracking using the Mean-Shift Tracking Algorithm

Takayuki Nishimori, Toyohiro Hayashi, Shuichi Enokida, and Toshiaki Ejima


Tracking, Image Understanding, Mean-shift, Histogram


An object tracking is one of the most important technique in digital image processing. The mean-shift tracking algorithm can be regarded as a specialization of template matching that scans locally according to the gradient of color features so that targets can be estimated faster. However, the mean-shift algorithm only guarantees a locally optimum solution, not a globally optimum solution. Thus, it is difficult to re-track the target automatically once tracking has failed. In this paper, to suppress the local optimal solution, a controlling method of the color histogram representation is proposed. The proposal method controls the color representation not to make the color histogram of a target area similar to background areas histograms. Actually, some experimental results showed the effectiveness of the proposal method.

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