【摘要】 为解决人体动作识别中的复杂背景和自遮挡问题,对深度图像进行研究,从深度图像中获取20个人体骨架关节点,在此基础上将动作时间序列的关节角度变化作为人体运动的特征模型。通过改进的动态时间规整算法计算不同动作之间关节角度变化序列的相似性,进行动作识别,以缓解传统DTW算法病态校准的问题。将识别方法在采集的动作数据库和MSR Action3D数据进行验证,实验结果表明,该方法能达到90%以上的识别率。更多还原

【Abstract】 Aiming at complex background and overlap problems in the human Action recognition task,depth images are used to obtain 20 joints in this paper. On the base of them,the joint-angle variation of action time series is proposed as human action feature model. Besides,this paper proposes an improved Dynamic Time Warping(DTW) algorithm to avoid the pathologic alignment that can be caused by original DTW algorithm. Then,the similarity between joint-angle variation series in different actions is calculated with improved DTW in order to recognize actions,i. e. template matching. The proposed method is carried out in self-collected action database and MSR Action3 D database.Experimental results show the proposed method gains recognition of more than 90 percent accuracy.更多还原