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Structured discriminant sparse representation based object tracking(PDF)


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Structured discriminant sparse representation based object tracking
Mao ZhengchongHuang Shuwei
Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education), Jiangnan University,Wuxi 214122,China
structured sparse representation object tracking scale-invariant feature transform classifiers dictionaries
An improved algorithm is proposed aiming at such shortcomings of sparse representation based object tracking algorithm as using an overall template and the poor ability of distinguishing targets from a background. Scale-invariant feature transform(SIFT)is used to extract the features of a target. Candidate objects are sparsely represented using appearance models of structured sparse representation,and sparse coefficients are obtained. A discriminant classifier is designed and trained by positive and negative samples,candidate objects are classified,and a confidence value is obtained. The tracking result of the previous frame is used to update the classifier and the dictionary. The improved algorithm is simulated. The average overlap ratio and average center point error of 3 test sequences of the simulation results are calculated,and Deer test sequence’s are 0.633 8 and 9.397 6,Car11 test sequence’s are 0.677 5 and 1.943 3,Caviar2 test sequence’s are 0.753 5 and 3.838 2.


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Last Update: 2018-06-30