一种基于RGB-D特征融合的人体行为识别框架

doi:10.11896/j.issn.1002-137X.2018.08.005

Abstract

Abstract: Human action recognition is an important research direction in the field of computer vision and pattern recognition.The complexity of human behavior and the variety of action performing make behavior recognition still as a challenging subject.With the new generation of sensing technology,RGB-D cameras can simultaneously record RGB images,depth images,and extract skeleton information from depth images in real time.How to take advantages of above information has become the new hotspot and breakthrough point of behavior recognition research.This paper presented a new feature extraction method based on Gaussian weighted pyramid histograms of orientation gradients for RGB images,and built an action recognition framework fusing multiple features.The feature extraction method and the framework proposed in this paper were researched on three databases:UTKinect-Action3D,MSR-Action 3D and Florence 3D Actions.The results indicate that the proposed action recognition framework achieves the accuracy of 97.5%,93.1%,91.7% respectively.It shows the effectiveness of the proposed action recognition framework.

Key words: Action recognition, Feature fusion, Gaussian weighted, Histogram of orientation gradients, Sparse representation classifier

CLC Number:

TP391

MAO Xia, WANG Lan, LI Jian-jun. Human Action Recognition Framework with RGB-D Features Fusion[J].Computer Science, 2018, 45(8): 22-27.

References

[1]XIA L,CHEN C C,Aggarwal J K.View Invariant Human Action Recognition Using Histograms of 3D Joints[C]∥Procee-dings of IEEE Conference on Computer Vision and Pattern Re-cognition.Providence:IEEE Press,2012:20-27.
[2]DALAL N,TRIGGS B.Histograms of Oriented Gradients for Human Detection[C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.San Diego:IEEE Press,2005:886-893.
[3]BOSCH A,ZISSERMAN A,MUMOZ X.Representing Shapewith a Spatial Pyramid Kernel[C]∥Proceedings of the Sixth ACM International Conference on Image and Video Retrieval.New York:ACM,2007:401-408.
[4]LUVIZON D C,TABIA H,PICARD D.Learning FeaturesCombination for Human Recognition from Skeleton Sequences[J/OL].(2017-02-02) [2017-07-06].http://dx.doi.org/10.1016/j.patrec.2017.02.001.
[5]YE J,LI K,QI G,et al.Temporal Order-Preserving DynamicQuantization for Human Action Recognition from Multimodal Sensor Streams[C]∥Proceedings of the Annual ACM International Conference on Multimedia Retrieval.Shanghai:ACM,2015:99-106.
[6]ZHANG H L,ZHONG P,HE J L,et al.Combining Depth-Ske-leton Feature with Sparse Coding for Action Recognition[J].Neurocomputing,2016,230(C):417-426.
[7]AHARON M,ELAD M,BRUCKSTEIN A.K-SVD:An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation[J].IEEE Transactions on Signal Processing,2006,54(11):4311-4322.
[8]JEGOU H,DOUZE M,SCHMID C,et al.Aggregating LocalDescriptors into a Compact Image Representation[C]∥Proceedings of the 23th IEEE Conference on Computer Vision and Pattern Recognition.California:IEEE Press,2010:3304-3311.
[9]WRIGHT J,YANG A Y,GANESH A,et al.Robust FaceRe-cognition via Sparse Representation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,31(2):210-227.
[10]LI W,ZHANG Z,LIU Z.Action Recognition Based on a Bag of 3D Points[C]∥Proceedings of the 23th IEEE Conference on Computer Vision and Pattern Recognition.San Francisco:IEEE Press,2010:9-14.
[11]SEUDEBARU L,VARANO V,BERRETTI S,et al.Recogni-zing Actions from Depth Cameras as Weakly Aligned Multi-part Bag-of-Poses[C]∥Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition.Portland:IEEE Press,2013:479-485.
[12]WANG J,LIU Z,WU Y,et al.Mining Actionlet Ensemble for Action Recognition with Depth Cameras[C]∥Proceedings of the 25th IEEE Conference on Computer Vision and Pattern Re-cognition.Providence:IEEE Press,2012:1290-1297.
[13]VEMULAPALLI R,ARRATE F,CHELLAPPA R,et al.Human Action Recognition by Representing 3D Skeletons as Points in a Lie Group[C]∥Proceedings of the 27th IEEE Conference on Computer Vision and Pattern Recognition.Columbus:IEEE Press,2014:588-595.
[14]DEVANNE M,WANNOUS H,BERRETTI S,et al.3-D Human Action Recognition by Shape Analysis of Motion Trajectories on Riemannian Manifold[J].IEEE Transactions on Systems,Man,and Cybernetics,2015,45(7):1340-1352.
[15]YIN F,JIAO L C,SHANG F,et al.Sparse Regularization Discriminant Analysis for Face Recognition[J].Neurocomputing,2014,128(5):341-362.

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Human Action Recognition Framework with RGB-D Features Fusion

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