快速运动和自遮挡是人体运动跟踪的难点所在.为此提出了一种采用弱预测机制的人体运动跟踪算法.该算法首先通过全局搜索，确定候选人体特征集 ;然后建立特征的色彩、运动等属性的时变模型，构造贝叶斯分类器，实现特征对应 ;最后根据人体特征层次模型，检验特征匹配，并实现被遮挡特征的定位.为提高跟踪效率，采用了基于图象多分辨率表示的特征搜索算法，由低分辨率图象通过全局搜索来获取初始候选特征集，然后在高分辨率下，不断改善候选特征精度.实验结果表明，该算法能实现对快速人体运动的跟踪并有效解决自遮挡问题.

It has been a challenge to capture rapid human motion with self occlusion. Current algorithms are not capable of tracking rapid motions with self occlusion: features with rapid motion are beyond small interest region search, and positions of the occluded features are difficult to be estimated. In this paper, we present a robust human motion tracking algorithm with weak prediction. Instead of predicting the position of each human feature, the region of the whole body is estimated and candidate features are extracted through the overall search in the estimated region. A multi resolution search strategy is proposed to improve the efficiency of overall search: the initial candidate feature set is extracted from the low resolution image and successively refined at higher resolution levels. To establish the correspondence between the candidate and the actual features, an adaptive Bayes Classifier is constructed based on the time varied models of feature attributions, viz. color and motion. And a hierarchical human feature model is adopted to verify and accomplish the feature correspondence. The experiment demonstrates the effectiveness of our algorithm.