作　　者： ; ; ;

机构地区： 中南大学信息科学与工程学院

出　　处： 《信息与控制》 2013年第4期457-463,共7页

摘　　要： 提出了一种基于连续空间关系模型(continuous-space relevance model,CRM)的人体动作识别算法.首先算法自动提取人体关节点位置.以骨架图像作为人体轮廓特征,使用动态时间规整(dynamic time wrapping,DTW)方法匹配相似骨架.利用相似骨架找到与目标轮廓相似的模板轮廓,并使用模板轮廓关节点位置估计目标关节点位置.最后以关节点轨迹作为人体动作特征,使用CRM对人体动作进行识别.该算法在KTH(Kungliga Tekniska Hegskolan)人体运动数据集、Weizmann人体动作数据集和Ballet数据集中进行了训练与测试.结果表明,该算法的识别精度与其它概率模型方法接近,甚至更高. A human action recognition algorithm based on continuous-space relevance model （CRM） is proposed. Firstly, the algorithm automatically extracts human joint position. The human silhouette is characterized by the skeleton graph which is matched by dynamic time wrapping （DTW）. The similar skeleton graph is used to find the template silhouette similar to the silhouette of the target, and the joint point position of the template silhouette is used to estimate the joint point position of the target. Finally, human actions are characterized by human joint point trajectories and recognized by CRM. This algorithm has been trained and tested on KTH human motion data set, Weizmann human action data set and Ballet data set. The results show that the recognition accuracy of this algorithm is either comparable to or much better than other sophisticated probabilistic models.

关 键 词： 人体动作识别 人体关节点 连续空间关系模型 动态时间规整

领　　域： [自动化与计算机技术] [自动化与计算机技术]