A fast scale estimation algorithm for visual tracking with feature integration is proposed to solve tracking failure from object scale changes. The gradient feature and color feature are integrated to obtain more object representation information with the increasing feature dimensions
then a fast multi-scale feature pyramid method is used to approximate the adjacent scale features to get templates in different scales
thus it is possible to balance the computation cost due to the increasing feature dimensions without accuracy loss after approximation. Combining tracking results of multi-scale templates
the object location and scale are estimated accurately by the proposed algorithm in the framework of correlation tracking 4 representative video sequences with scale changes
and illumination variations and background clusters are chosen to simulate. The experiments indicate that the proposed algorithm well adapts to environmental variations and outperforms the traditional scale-adaptive kernel correlation tracking schemes in center location error
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