Abstract
To benefit from the development of compressive sensing, we cast tracking as a sparse approximation problem in a particle filter framework based on multifeatures. In this framework, the target template is composed of multiple features extracted from visible and infrared frames; in addition, occlusion, interruption, and noises are addressed through a set of trivial templates. With this model, the sparsity is achieved via a compressive sensing approach without nonnegative constraints; then the residual between sparsity representation and the compressed sensing observation is used to measure the likelihood which weights particles. After that, the target template is adaptively updated according to the Bhattacharyya coefficients. Some experimental results demonstrate that the proposed tracker appears to have better robustness compared with four different algorithms.
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