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A Mean Shift Video Tracking Algorithm Based Haar-like Feature and Color Feature

Lei Guo, Junfeng Wu


Due to the fact that traditional color feature cannot extract sufficient detail information of target, error tracking is liable to happen when target is blocked by objects that are of similar color distribution with target. This article introduces Haar-like Feature that can extract detail information of target and describes the integration of Haar-like Feature and Color Feature that is applied in Mean Shift Algorithm as the extracting feature. On account of Haar feature’s common application in grayscale images, Haar-like feature is only applied in Subspace I which indicates brightness in HSI Color-space. For the other two Subspaces in HSI Color-space, the color feature remains as description of objects. Upon comparative experiments, it could be concluded that the tracking effectiveness of integrated features of Haar-like feature and Color feature is greater than that of color feature as for Mean Shift Video Tracking Algorithm.


mean shift, haar-like feature, object tracking.

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