In this paper,we propose a novel visual tracking method using a discriminative representation under a Bayesian framework.First,we exploit the histogram of gradient (HOG)to generate the texture features of the target t...In this paper,we propose a novel visual tracking method using a discriminative representation under a Bayesian framework.First,we exploit the histogram of gradient (HOG)to generate the texture features of the target templates and candidates.Second,we introduce a novel discriminative representation and l2-regularized least squares method to solve the proposed representation model.The proposed model has a closed-form solution and very high computational efficiency.Third,a novel likelihood function and an update scheme considering the occlusion factor are adopted to improve the tracking performance of our proposed method. Both qualitative and quantitative evaluations on 15 challenging video sequences demonstrate that our method can achieve more robust tracking results in terms of the overlap rate and center location error.展开更多
基金Shandong Province Higher Educational Science and Technology Program (J17KA088 and J16LN02)the Natural Science Foundation of Shandong Province (ZR2015FL009 and ZR2014FL020)+1 种基金the Key Research and Development Program of Shandong Province (2016GGX101023)and the Science Foundation of Binzhou University (BZXYG1524).
文摘In this paper,we propose a novel visual tracking method using a discriminative representation under a Bayesian framework.First,we exploit the histogram of gradient (HOG)to generate the texture features of the target templates and candidates.Second,we introduce a novel discriminative representation and l2-regularized least squares method to solve the proposed representation model.The proposed model has a closed-form solution and very high computational efficiency.Third,a novel likelihood function and an update scheme considering the occlusion factor are adopted to improve the tracking performance of our proposed method. Both qualitative and quantitative evaluations on 15 challenging video sequences demonstrate that our method can achieve more robust tracking results in terms of the overlap rate and center location error.
