Compressed sensing shows that one undetermined measurement matrix can losslessly compress sparse signals if this matrix satisfies Restricted Isometry Property (RIP). However, in practice there are still no explicit approaches to construct such matrices. Gaussian matrices and Fourier matrices are first proved satisfying RIP with high probabilities. Recently, sparse random binary matrices with lower computation load also expose comparable performance with Gaussian matrices. But they are all constructed randomly, and unstable in orthogonality. In this paper, inspired by these observations, we propose to construct structured sparse binary matrices which are stable in orthogonality. The solution lies in the algorithms that construct parity-check matrices of low-density parity-check (LDPC) codes. Experiments verify that proposed matrices significantly outperform aforementioned three types of matrices. And significantly, for this type of matrices with a given size, the optimal matrix for compressed sensing can be approximated and constructed according to some rules.

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Recently sparse representation has been successfully applied to single object tracking by observing the reconstruction error of candidate object with sparse representation. In practice, sparse representation also shows competitive performance on multi-class classification, and thus is potential for multi-object tracking. In this paper we explore this technique for on-line multi-object tracking through a simple trackingby-detection scheme, with background subtraction for object detection and sparse representation for object recognition. Final experiments demonstrate that the proposed approach only combining color histogram and 2-dimensional coordinates as features, achieves favorable performance over state-of-the-art work in persistent identity tracking.