2DPCA与稀疏表示模型的运动目标跟踪法

为了提高目标跟踪的准确性，针对当前目标跟踪算法的光照、遮挡以及姿态变化鲁棒性差等问题，提出了一种二维主成分分析和稀疏表示的目标跟踪算法。采用二维主成分分析和稀疏表示降低数据维数，减少计算复杂度，采用粒子滤波算法跟踪序列图像中的运动目标，采用仿真实验测试算法的性能。仿真结果表明，相对于其他运动目标跟踪算法，该算法可以更准确跟踪视频图像中的运动目标，并对光照和姿态变化具有良好的鲁棒性，对于严重遮挡目标跟踪问题，具有明显的优势。

Object tracking algorithm based on 2DPCA and sparse representation

In order to improve the target tracking accuracy, for the tracking algorithm’s illumination, occlusion and pose variation problem of poor robustness, tracking algorithm is presented for two-dimensional principal component analysis and sparse representation of the target. The sparse representation of the two-dimensional principal component is analized to reduce the dimension of data, and to reduce the computational complexity; the particle filter algorithm is used to track moving target in image sequences; the algorithm is tested in simulation experiment for its performance. The simulation results show that, compared with other video target tracking algorithm, this algorithm can track moving target more reliably than other algorithm in image sequences, and has good robustness and the appearance from changes caused by the process of target tracking of illumination and pose variation. The algorithm has obvious advantages in more serious occlusion target tracking case.