We construct a collaborative model of the sparse representation and the subspace representation. First, we represent the tracking target in the principle component analysis(PCA) subspace, and then we employ an L_1 reg...We construct a collaborative model of the sparse representation and the subspace representation. First, we represent the tracking target in the principle component analysis(PCA) subspace, and then we employ an L_1 regularization to restrict the sparsity of the residual term, an L_2 regularization term to restrict the sparsity of the representation coefficients, and an L_2 norm to restrict the distance between the reconstruction and the target. Then we implement the algorithm in the particle filter framework. Furthermore, an iterative method is presented to get the global minimum of the residual and the coefficients. Finally, an alternative template update scheme is adopted to avoid the tracking drift which is caused by the inaccurate update. In the experiment, we test the algorithm on 9 sequences, and compare the results with 5 state-of-art methods. According to the results, we can conclude that our algorithm is more robust than the other methods.展开更多
基金supported by the National Natural Science Foundation of China(No.61401425)
文摘We construct a collaborative model of the sparse representation and the subspace representation. First, we represent the tracking target in the principle component analysis(PCA) subspace, and then we employ an L_1 regularization to restrict the sparsity of the residual term, an L_2 regularization term to restrict the sparsity of the representation coefficients, and an L_2 norm to restrict the distance between the reconstruction and the target. Then we implement the algorithm in the particle filter framework. Furthermore, an iterative method is presented to get the global minimum of the residual and the coefficients. Finally, an alternative template update scheme is adopted to avoid the tracking drift which is caused by the inaccurate update. In the experiment, we test the algorithm on 9 sequences, and compare the results with 5 state-of-art methods. According to the results, we can conclude that our algorithm is more robust than the other methods.
