中文摘要：

区别于以往采用固定运动模式的目标跟踪研究,提出一种基于单目视觉传感器的人体运动模式在线识别算法,及基于此算法的人体目标跟踪方法。首先,利用视觉信息检测运动目标,并提取其视觉特征;然后通过单目视觉深度提取算法,获取目标的运动特征;接着将连续几帧的特征变化矢量送入随机森林（RF）进行学习,实现对人体运动模式的在线分类;最后根据分类结果在线选取不同的目标运动模型,并利用近似最优的粒子滤波器实现对目标运动状态的准确估计。实验结果证明了本文提出算法的有效性。

英文摘要：

Different from the existing researches on target tracking using a fixed motion pattern,an algorithm is proposed for on-line recognition of human motion patterns with a monocular camera,to develop a human tracking method. First,moving target is detected using visual information and the visual features are extracted. Then,the motion features of target are acquired by depth extraction algorithm of monocular vision. The differences of a couple of successive frames＇ features are fed into random forest（ RF） classifier to recognize human motion patterns online. Finally,different target motion models are chosen online based on classification results and approximate optimal particle filter is used to realize accurate estimation of human＇s states. Extensive experimental results demonstrate the effectiveness of the proposed algorithm.