关键词: 反向稀疏, 贝叶斯估计, 分段加权, 目标跟踪

Abstract: To improve the performance of sparse representation tracking model, a piecewise weighted inverse sparse tracking algorithm is proposed, which translates the tracking problem into finding the most probable candidate target within Bayesian framework. Different piecewise weighted functions are constructed to separately measure the discriminant characteristic coefficients of the candidate target with the positive and negative templates. The pooling is utilized to reduce the uncertainty of the tracking results of interference, then the candidate represented by the biggest difference between the positive and negative template weight coefficients is chosen as the tracking result. Experiments indicate that the proposed algorithm can ensure the accuracy and robustness of tracking results in case of the light changes, occlusion, fast motion, motion and blur.

Key words: reverse sparse, Bayesian estimation, piecewise weighted, target tracking