In order to improve the stability and accuracy of the object tracking under nonlinear conditions, an object tracking algorithm based on Partial Least Squares (PLS) representation and stochastic gradient object optimization tracking is proposed. In this method, object tracking is defined as an optimization task that minimizes the representation error and classification loss. Firstly, it expresses object appearance and background information by PLS theory, learns multiple low dimensional and discriminative subspaces to describe the nonlinear appearance changes of the object. Then, a joint optimization objective function based on deterministic search mechanism is proposed. Furthermore, an stochastic gradient classifier based on incremental features updating is proposed, and make sure that it can achieve a stable tracking. Experiments show favorable performance of the proposed algorithm on sequences where the targets undergo a variety complex changes on foreground and background.
金广智,石林锁,刘浩,牟伟杰,蔡艳平. 结合PLS表示与随机梯度的目标优化跟踪[J]. 电子与信息学报, 2016, 38(8): 2027-2032.
JIN Guangzhi, SHI Linsuo, LIU Hao, MU Weijie, CAI Yanping. Object Optimization Tracking via PLS Representation and Stochastic Gradient. JEIT, 2016, 38(8): 2027-2032.
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