基于混沌不变量和关联向量机的人体行为识别

提出了一种基于混沌不变量特征和关联向量机（RVM）的人体行为识别方法.提取人体关节点运动产生的轨迹代表人体动作行为的非线性系统，利用 C-C方法估计时延并且得到由每条运动轨迹重构的相空间维数，并从重构的相空间提取代表人体行为的混沌不变量，利用RVM算法识别人体行为.在KTH，Weizmann及ballet 数据库中进行测试，实验结果表明，使用该方法平均正确率达92.1%.

Recognition of Human Behavior Based on Chaos Invariant and Relevance Vector Machine

An algorithm is put forward for the recognition of human behavior based on chaos invariant characteristics and relevance vector machine(RVM).First,the motion track generated by the joints of human is extracted to represent the nonlinear system of human movement behavior   and C-C method is used to estimate the delay to obtain the dimension of phase space reconstituted by every motion track.Furthermore,the chaos invariant which represents human behavior is extracted from the phase space and RVM algorithm is used to recognize human behavior.Finally KTH,Weizmann human behavior database and ballet database are applied to test the effect of the algorithm,and the result proves that this method has the better recognition effect than others.