权重融合深度图像与骨骼关键帧的行为识别  

Action Recognition Using Weighted Fusion of Depth Images and Skeleton's Key Frames

作　　者：许艳[1] 侯振杰[1] 梁久祯[1] 陈宸 贾靓[1] 宋毅 Xu Yan;Hou Zhenjie;Liang Jiuzhen;Chen Chen;Jia Liang;Song Yi(College of Information Science and Engineering,Changzhou University,Changzhou 213164;Department of Electrical Engineering,University of Texas at Dallas,Richardson,TX 75080 USA)

机构地区：[1]常州大学信息科学与工程学院,常州213164 [2]Department of Electrical Engineering, University of Texas at Dallas

出　　处：《计算机辅助设计与图形学学报》2018年第7期1313-1320,共8页Journal of Computer-Aided Design & Computer Graphics

基　　金：国家自然科学基金(61063021);江苏省产学研前瞻性联合研究项目(BY2015027-12)

摘　　要：针对2D信息量不足导致人体行为识别率不高的问题,提出融合多种深度信息的行为识别方法.首先利用深度图像捕捉行为线索,提取梯度及相关方向特征;然后利用互信息提取骨骼图的关键帧,提出基于关键帧的静态姿态模型、当前运动模型和动态偏移模型表征人体行为底层特征;最后通过权重投票机制为不同种类特征分配权重,实现多类特征下的多权重融合.在MSR_Action3D深度数据集上的实验结果表明,该方法的识别率比其他方法提高1.5%.Aiming at the poor recognition performance caused by insufficient two-dimensional information, a human action recognition method by fusing multiple depth information is proposed. Firstly, the depth images are used to capture the behavior clues and extract gradient and related directional features. Then, it uses mutual information to extract key frames of skeleton images. The static attitude model, the current motion model and the dynamic offset model based on the key frames are established to characterize the underlying features of human action. Finally, weights are assigned to different kinds of features through weighted voting mechanism, which realizes multiple weighted fusion with multiple features. Experiments conducted on MSR_Action3D depth action dataset show the accuracy of this proposed method is 1.5% higher than the state-of-the-art action recognition methods.

关 键 词：深度图像 骨骼图像 行为识别 互信息 权重投票 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]