In order to complete the task of real-time object tracking, this paper proposes a new local texture description operator, which is called center symmetric-local similarity number(CS-LSN). The operator analyzed the center pixel and its eight neighborhood pixels, on the basis of the local similarity number (LSN); as the texture operator fails to distinguish the same local saliency of different texture structures, the authors added the local gradient information using the center pixel as the symmetric point, extracted the key pixels which correspond only to the five major patterns of the CS-LSN in the target candidate region, effectively restrained the influence of the background pixels and reduced the computation in the target representation model, and then represented target by CS-LSN texture feature and the chromaticity in the true target pixel, which was embedded into the mean shift(MS) tracking framework. The experimental results show that the proposed method can continuously track the target when the background and the color are similar under the condition of changing illumination and in the occlusion cases. The processing speed can reach 25 frames or so per second when the object is 29 pixel×25 pixel, so it can satisfy the demand of real-time application.
刘威, 赵文杰, 李成, 李婷, 谭海峰, 马扬铭. 基于中心对称局部相似数量模型的均值漂移目标跟踪[J]. 国土资源遥感, 2016, 28(3): 37-45.
LIU Wei, ZHAO Wenjie, LI Cheng, LI Ting, TAN Haifeng, MA Yangming. Mean shift object tracking based on center symmetric-local similarity number model. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(3): 37-45.
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2016, Vol. 28 
