基于粒子滤波与局部全局一致性学习的目标跟踪算法

针对目标形变及复杂背景条件下的目标跟踪问题，利用基于图的半监督学习方法，结合粒子滤波，提出一种自适应的目标跟踪算法。该算法利用局部全局一致性学习（LLGC）算法建立代价函数，将当前的候选状态作为未标记样本，以所有样本为顶点建立图，以代价函数的最优解作为当前的状态，从而得到当前帧的目标位置；同时利用跟踪结果对标记样本进行实时更新，以适应目标形变，部分遮挡以及环境光照的变化。实验结果表明，该方法能够很好地处理目标跟踪中常见的遮挡、相似背景干扰等复杂情形，实现对目标的鲁棒跟踪

Target tracking algorithm based on particle filter and learning with local and global consistency

To solve target tracking with target changes under complex background, an adaptive target tracking method that combined graph-based semi-supervised learning method with the particle filter was proposed. It used LLGC (Learning with Local and Global Consistency) algorithm to establish the cost function, and took current status of the candidate as unlabeled samples, then established diagram using all samples as vertex, taking the optimal solution of the cost function as current status, obtaining the target position in current frame. Besides, it used the tracking result to update the labeled samples in real time, so that the algorithm could adapt to the target deformation, partial occlusion and illumination changes. Analysis and experiment show that the proposed method can handle complicated situations like occlusion or similar background interference very well, and achieves target tracking robustly.