Abstract:

Object tracking is an important research area in computer vision. Researchers have proposed a number of excellent object tracking algorithms in recent years. However, the poor realtime performance of these algorithms restricts its effectiveness in application scenarios. Based on these algorithms, we design a general tracking model and propose a feasible parallel optimization scheme for the model. We also use the sparsitybased collaborative model (SCM) algorithm to validate the proposed scheme. In a fourcore CPU environment, the parallel algorithm achieves a speedup of 3.48 times compared with the sequential algorithm, and it is about 30 times faster than the MATLAB+C program of the original algorithms, thus verifying the effectiveness of the proposed parallel optimization scheme.

Key words: object tracking, tracking model, parallel optimization