针对目标跟踪在遮挡和尺度变化等复杂背景下跟踪性能下降问题,联合稀疏约束、时间平滑约束以及增量投影非负矩阵分解,提出一种在线目标跟踪算法.首先利用非负矩阵分解学习一个基于部分表示的子空间,在此基础上添加稀疏约束提高处理遮挡能力,添加时间平滑约束提高算法的稳定性;然后用增量方式完成子空间的在线更新,减少算法计算量、提高外观模型更新效率;最后在粒子滤波框架下,以重构误差为基础改进了观测似然函数,将具有最大后验概率的候选目标作为目标在当前帧的图像区域.实验结果表明,在各种含有遮挡和尺度变化的视频中,该算法可以更稳定地跟踪目标.%The performance of existing object tracking algorithms decreases obviously when the object undergoes partial or full occlusion, and posture change with complex background. To handle these problems, an online in-cremental projection non-negative matrix factorization based object tracking algorithm with sparse and time smooth constraints is proposed. The local structure of the target is represented by the basis matrix, which is ex-tracted by non-negative matrix factorization along with the sparse constraint to deal with a variety of challenging scenarios and the time smooth constraint to improve the tracking robustness. The incremental basis matrix updat-ing strategy reduces the amount of computation evidently, resulting in the appearance model updating more effi-ciently. In the particle filter framework, the observation likelihood function is modified based on the reconstruc-tion error of candidates when projected to the basis matrix, the candidate with a max posteriori probability is rec-ognized as the target in the current frame. Experimental results on various video sequences show that, compared with the state-of-the-art tracking methods, the proposed algorithm achieves favorable performance when the ob-ject has large occlusion or scale variation.
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