关键词: 视觉跟踪, 稀疏跟踪算法, 在线判别分析

Abstract: By analyzing the distribution of the particles sampled from particle filter and their difference with the true states of the moving target, a robust sparse tracking method based on online logistic discriminate learning is proposed. The discrimination of the moving target and backgrounds is enhanced by the discriminating and updating procedures of the logistic function. The proposed method is more efficient than the original sparse tracking since the particles with little relevancy are rejected in advance of the L1 optimization. Experimental results demonstrate that, with 5 state-of-the-art tracking under 4 challenging video sequences, the proposed tracking method obtains the most robust results, and its running time is almost equivalent with the BPR-L1 tracking method.

Key words: visual tracking, sparse tracking, online discriminate learning