Part-based Object Tracking Based on Multi Collaborative Model
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TP391

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National Natural Science Foundation of China (61472196, 61672305); Key Research and Development Program of Shandong Provience (2017GGX10133)

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    Abstract:

    A part-based tracking approach based on multi collaborative model is proposed that can address the problem of losing object based on the holistic appearance model in complex scenarios. Object appearance model is constructed by fusing the generative model based on local sensitive histogram (LSH) and discriminative model based on superpixel segmentation, by extracting the illumination invariant feature of the LSH resist the influence of the illumination changes on the object model effectively; for the lack of effective occlusion handling mechanism of the LSH algorithm, the part-based adaptive model segmentation method is introduced to improve the performance of resistance occlusion; by through the relative entropy and mean shift cluster method, measuring the differences confidence value and the foreground-background confidence value of the local part, establish the dual weights constraint mechanism and asynchronous update strategy for the part model, the partes with high confidence are selected to locate object in the particle filter framework. Experimental results on challenging sequences confirm that the proposed approach outperforms the related tracking algorithm in complex scenarios.

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刘明华,汪传生,胡强,王传旭,崔雪红.多模型协作的分块目标跟踪.软件学报,2020,31(2):511-530

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History
  • Received:May 22,2017
  • Revised:May 28,2018
  • Adopted:
  • Online: March 28,2019
  • Published: February 06,2020
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