Visual Tracking with Sparse Prototypes: An Approach Based on Variational Bayesian Inference

Due to its ability to model image corruptions explicitly, sparse representation (SR) attracts much attention from the community of visual tracking in recent years. However, the existing tracking approaches generally employ the l(1) norm regularization method to achieve sparse recovery. As a result, they have to set the regularization parameter to an appropriate value, which is actually a crucial but difficult task in practice. To avoid this difficulty, we develop a new algorithm to solve the resultant SR problem under the framework of variational Bayesian inference. The algorithm can simultaneously estimate the sparse coefficients and other unknown parameters in an automatic manner. Consequently, using this algorithm to achieve SR involves little user intervention. In addition, we employ a new observation likelihood function to allow for the incorporation of a particle screening mechanism into the tracking process. Based on the above modifications, we develop a new algorithm for visual tracking based on sparse prototypes. Experimental results over various video sequences demonstrate the effectiveness and robustness of the algorithm.