[1]GONG Dongying,HUANG Min,ZHANG Hongbo,et al.Adaptive feature selection method for action recognition of human body in RGBD data[J].CAAI Transactions on Intelligent Systems,2017,12(1):1-7.[doi:10.11992/tis.201611008]
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Adaptive feature selection method for action recognition of human body in RGBD data

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 12 Number of periods: 2017 1 Page number: 1-7 Column: 学术论文—机器感知与模式识别 Public date: 2017-02-25
Title:
Adaptive feature selection method for action recognition of human body in RGBD data
Author(s):
GONG Dongying1; 2;  HUANG Min1; 2;  ZHANG Hongbo3;  LI Shaozi1; 2
1. Intelligent Science & Technology Department, Xiamen University, Xiamen 361005, China;
2. Fujian Key Laboratory of Brain-like Intelligent Systems, Xiamen University, Xiamen 361005, China;
3. Computer Science & Technology School, Huaqiao University, Xiamen 361005, China
Keywords:
action recognition of human body; adaptive feature selection; information entropy; random forest
CLC:
TP391.41
DOI:
10.11992/tis.201611008
Abstract:
Many methods adopt the technique of multi-feature fusion to improve the recognition accuracy of RGBD video. Experimental analyses revealed that the classification effect of certain behavior in some features is good; however, multi-feature fusion cannot reflect the classification superiority of certain features. Moreover, multi-feature fusion is highly dimensional and considerably expensive in terms of time and space. This research proposes an adaptive feature selection method for RGBD human-action recognition to solve this problem. First, random forest and information entropy were used to analyze the judgment ability of the human joints, whereas the number of human joints with high judgment ability were chosen as the feature selection criterion. By screening the threshold number, either the joint feature or the relative positions of the joints was used as the recognition feature of action. Experimental results show that compared with multi-feature fusion, the method significantly improved the accuracy of action recognition and outperformed most other algorithms.
References:
[1] WANG Jiang, LIU Zicheng, WU Ying, et al. Mining actionlet ensemble for action recognition with depth cameras[C]//Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Providence, USA, 2012: 1290-1297.
[2] YANG Xiaodong, TIAN Yingli. Super normal vector for activity recognition using depth sequences[C]//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, USA, 2014: 804-811.
[3] CHEN Chen, JAFARI R, KEHTARNAVAZ N. Action recognition from depth sequences using depth motion maps-based local binary patterns[C]//Proceedings of 2015 IEEE Winter Conference on Applications of Computer Vision. Waikoloa, USA, 2015: 1092-1099.
[4] XIA LU, CHEN C C, AGGARWAL J K. View invariant human action recognition using histograms of 3D joints[C]//Proceedings of 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Providence, USA, 2012: 20-27.
[5] LIU Jingen, ALI S, SHAH M. Recognizing human actions using multiple features[C]//Proceedings of 2008 IEEE Conference on Computer Vision and Pattern Recognition. Anchorage, USA, 2008: 1-8.
[6] WANG Liang, ZHOU Hang, LOW S C, et al. Action recognition via multi-feature fusion and Gaussian process classification[C]//Proceedings of 2009 Workshop on Applications of Computer Vision. Snowbird, USA, 2009: 1-6.
[7] LIU Jia, YANG Jie, ZHANG Yi, et al. Action recognition by multiple features and hyper-sphere multi-class SVM[C]//Proceedings of the 20th International Conference on Pattern Recognition. Istanbul, Turkey, 2010: 3744-3747.
[8] BENMOKHTAR R. Robust human action recognition scheme based on high-level feature fusion[J]. Multimedia tools and applications, 2014, 69(2): 253-275.
[9] TRAN K, KAKADIARIS I A, SHAH S K. Fusion of human posture features for continuous action recognition[C]//Proceedings of the 11th European Conference on Trends and Topics in Computer Vision. Heraklion, Greece, 2010: 244-257.
[10] OREIFEJ O, LIU Zicheng. HON4D: histogram of oriented 4D normals for activity recognition from depth sequences[C]//Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition. Portland, USA, 2013: 716-723.
[11] YANG Xiaodong, TIAN Yingli. Effective 3D action recognition using EigenJoints[J]. Journal of visual communication and image representation, 2014, 25(1): 2-11.
[12] RAHMANI H, MAHMOOD A, HUYNH D Q, et al. Real time action recognition using histograms of depth gradients and random decision forests[C]//Proceedings of 2014 IEEE Winter Conference on Applications of Computer Vision. Steamboat Springs, USA, 2014: 626-633.
[13] YU Gang, LIU Zicheng, YUAN Junsong. Discriminative orderlet mining for real-time recognition of human-object interaction[M]//CREMERS D, REID I, SAITO H, et al. Computer Vision—ACCV 2014. Lecture Notes in Computer Science. Cham: Springer International Publishing, 2015: 50-65.
[14] CHAARAOUI A A, PADILLA-LOPEZ J R, FLOREZ-REVUELTA F. Fusion of skeletal and silhouette-based features for human action recognition with RGB-D devices[C]//Proceedings of 2013 IEEE International Conference on Computer Vision Workshops. Sydney, Australia, 2013: 91-97.
[15] GAO Zan, ZHANG Hua, LIU A A, et al. Human action recognition on depth dataset[J]. Neural computing and applications, 2016, 27(7): 2047-2054.
[16] LIU Zhi, ZHANG Chenyang, TIAN Yingli. 3D-based deep convolutional neural network for action recognition with depth sequences[J]. Image and vision computing, 2016, 55(2): 93-100.
[17] LI Meng, LEUNG H, SHUM H P H. Human action recognitionvia skeletal and depth based feature fusion[C]//Proceedings of the 9th International Conference on Motion in Games. Burlingame, USA, 2016: 123-132.
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