Particle Filter Object Tracking Algorithm Based on Sparse Representation and Nonlinear Resampling

Object tracking with abrupt motion is an important research topic and has attracted wide attention. To obtain accurate tracking results, an improved particle filter tracking algorithm based on sparse representation and nonlinear resampling is proposed in this paper. First, the sparse representation is used to compute particle weights by considering the fact that the weights are sparse when the object moves abruptly, so the potential object region can be predicted more precisely. Then, a nonlinear resampling process is proposed by utilizing the nonlinear sorting strategy, which can solve the problem of particle diversity impoverishment caused by traditional resampling methods. Experimental results based on videos containing objects with various abrupt motions have demonstrated the effectiveness of the proposed algorithm.