Object Tracking Method Based on Superpixel and Local Sparse Representation

Abstract

Abstract: Due to the appearance changing of target object in object tracking, a tracking algorithm was proposed based on superpixel and local sparse representation (SPS). In training process, a discriminative appearance model was constructed by clustering the segmented train images; sparsity-based histogram of target object was calculated to construct generative appearance model. In tracking, superpixel-based confidence map was obtained, and the confidence values of candidates was sampled and calculated; the similarity between sparsity-based histogram of candidates and target template was computed by using local patches. Then motion model and observation model of candidates according to the confidence values and similarity of candidates were computed, which obtained maximum a posterior estimate of the samples and determined the track result. Furthermore, online updating of the two appearance model was kept independently. The experimental results and evaluations demonstrate that application of SPS algorithm can obtain accurate and robust track result with the appearance variation of target object.

Key words: superpixel, confidence map, sparse representation, appearance mode

CLC Number:

TP391.9

Yang Huixian, Liu Zhao, Liu Yang, Liu Fan, He Dilong. Object Tracking Method Based on Superpixel and Local Sparse Representation[J]. Journal of System Simulation, 2016, 28(5): 1017-1030.

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