Abstract
In this paper we present an algorithm research on moving object detection and tracking in video sequence using color feature. In this algorithm we combine between the probability product kernels as a similarity measure, and the integral image to compute the histograms of all possible target regions of object tracking in data sequence. The objective of this algorithm is to associate target object in consecutive video frames. The association can be especially difficult when the objects are moving fast relative to the frame rate. Another situation that increases the complexity of the problem is when the tracked object changes orientation over time. For these situations the proposed algorithm is used to improve the tracking accuracy and decrease the tracking failures in the video tracking process, and usually employ a motion model which describes how the image of the target might change for different possible motions of the object.
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