Combining Multi-scale Directed Depth Motion Maps and Log-Gabor Filters for Human Action Recognition  

作　　者：Xiaoye Zhao Xunsheng Ji Yuanxiang Li Li Peng 

机构地区：[1]Engineering Research Center of Internet of Things Technology Applications of the Ministry of Education, School of Internet of Things Engineering, Jiangnan University

出　　处：《Journal of Harbin Institute of Technology(New Series)》2019年第4期89-96,共8页哈尔滨工业大学学报（英文版）

基　　金：Sponsored by the Jiangsu Prospective Joint Research Project(Grant No.BY2016022-28)

摘　　要：Recognition of the human actions by computer vision has become an active research area in recent years. Due to the speed and the high similarity of the actions, the current algorithms cannot get high recognition rate. A new recognition method of the human action is proposed with the multi-scale directed depth motion maps(MsdDMMs) and Log-Gabor filters. According to the difference between the speed and time order of an action, MsdDMMs is proposed under the energy framework. Meanwhile, Log-Gabor is utilized to describe the texture details of MsdDMMs for the motion characteristics. It can easily satisfy both the texture characterization and the visual features of human eye. Furthermore, the collaborative representation is employed as action recognition by the classification. Experimental results show that the proposed algorithm, which is applied in the MSRAction3 D dataset and MSRGesture3 D dataset, can achieve the accuracy of 95.79% and 96.43% respectively. It also has higher accuracy than the existing algorithms, such as super normal vector(SNV), hierarchical recurrent neural network(Hierarchical RNN).Recognition of the human actions by computer vision has become an active research area in recent years. Due to the speed and the high similarity of the actions, the current algorithms cannot get high recognition rate. A new recognition method of the human action is proposed with the multi-scale directed depth motion maps(MsdDMMs) and Log-Gabor filters. According to the difference between the speed and time order of an action, MsdDMMs is proposed under the energy framework. Meanwhile, Log-Gabor is utilized to describe the texture details of MsdDMMs for the motion characteristics. It can easily satisfy both the texture characterization and the visual features of human eye. Furthermore, the collaborative representation is employed as action recognition by the classification. Experimental results show that the proposed algorithm, which is applied in the MSRAction3 D dataset and MSRGesture3 D dataset, can achieve the accuracy of 95.79% and 96.43% respectively. It also has higher accuracy than the existing algorithms, such as super normal vector(SNV), hierarchical recurrent neural network(Hierarchical RNN).

关 键 词：human action recognition DEPTH MOTION MAPS LOG-GABOR filters collaborative representation based CLASSIFIER 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]