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| Human Action Dynamic Modeling Recognition Based on Spatial Distribution Feature |
| LIN Guang-Feng1,ZHU Hong2,FAN Cai-Xia1,ZHANG Er-Hu1 |
1.Department of Information Science,Xi′an University of Technology,Xi′an 710048
2.Faculty of Automation and Information Engineering,Xi′an University of Technology,Xi′an 710048 |
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Abstract The appearance feature and dynamic feature of human action have not an integrate description,which leads to distinguish human action inaccurately. In this paper,human action dynamic modeling recognition based on the spatial distribution feature (DMRSD) is proposed. Firstly,the spatial region of the feature is divided into a number of local regions by the relative polar coordinates,the statistic number of the nonzero information points is obtained in these local regions,and these numbers form a spatial distribution feature which describes the action appearance feature. Then,these spatial distribution feature sequences are modeled by autoregressive moving average model,then the feature of model parameter is obtained,which represents the dynamic time structure. Finally,the linear relation of the affinity matrix of these parameter features is hypothesized,the appearance feature structure and the dynamic motion feature structure are fused,and an integrate description is generated. Human action recognition is directly performed on the fusion structure of an integrate description by the nearest neighbor classification. Compared to the recognition results of current methods,DMRSD obtains better recognition rate on Weizmann and KTH databases.
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Received: 30 August 2012
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[1\]Poppe R. A Survey on Vision Based Human Action Recognition. Image and Vision Computing,2010,28(6): 976-990
[2]Weinland D,Ronfard R,Boyer E. A Survey of Vision Based Methods for Action Representation,Segmentation and Recognition. Computer Vision and Image Understanding,2011,115(2): 224-241
[3]Weinland D,Ronfard R,Boyer E. Free Viewpoint Action Recognition Using Motion History Volumes. Computer Vision and Image Understanding,2006,104(2): 249-257
[4]Gorelick L,Blank M,Shechtman E,Xet al. Actions as Space Time Shapes. IEEE Trans on Pattern Analysis and Machine Intelligence,2007,29(12): 2247-2253
[5]Laptev I,Caputo B,Schüldt C,Xet al. Local Velocity Adapted Motion Events for Spatio Temporal Recognition. Computer Vision and Image Understanding,2007,108(3): 207-229
[6]Juan C N,Wang Hongcheng,Li Feifei. Unsupervised Learning of Human Action Categories Using Spatial Temporal Words. International Journal of Computer Vision,2008,79(3): 299-318
[7]Zhang Jianguo,Gong Shaogang. Action Categorization with Modified Hidden Conditional Random Field. Pattern Recognition,2010,43(1): 197-203
[8]Ali S,Basharat A,Shah M. Chaotic Invariants for Human Action Recognition // Proc of the IEEE 11th International Conference on Computer Vision. Rio de Janeiro,Brazil,2007: 1-8
[9]2Basharat A,Shah M. Time Series Prediction by Chaotic Modeling of Nonlinear Dynamical Systems // Proc of the IEEE 12th International Conference on Computer Vision. New York,USA,2009: 1941-1948
[10]Pavan T,Ashok V,Anuj S,Xet al. Statistical Computations on Grassmann and Stiefel Manifolds for Image and Video Based Recognition. IEEE Trans on Pattern Analysis and Machine Intelligence,2011,33(11): 2273-2286
[11]Gaidon A,Harchaoui Z,Schmid C. A Time Series Kernel for Action Recognition // Proc of the British Machine Vision Conference. Dundee,UK,2011: 1-11
[12]2Wang Heng,Klser A,Schmid C,Xet al. Action Recognition by Dense Trajectories // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Beijing,China,2011: 3169-3176
[13]Liang Wang,Suter D. Recognizing Human Activities from Silhouettes: Motion Subspace and Factorial Discriminative Graphical Model // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis,USA,2007: 1-8
[14]Zhang Ziming,Hu Yiqun,Chan Syin,Xet al. Motion Context: A New Representation for Human Action Recognition. Lecture Notes in Computer Science,2008,5305: 817-829
[15]Xin Shu,Wu Xiaojun. A Novel Contour Descriptor for 2D Shape Matching and Its Application to Image Retrieval. Image and Vision Computing,2011,29(4): 286-294
[16]Lin Guangfeng,Zhu Hong,Fan Caixia,Xet al. Object Segmentation Based on Guided Layering from Video Image. Optical Engineering,2011,50(9): 1-8
[17]Horn B K P,Schunck B G. Determining Optical Flow. Artificial Intelligence,1981,17(1/2/3): 185-203
[18]de Cock K,de Moor B. Subspace Angles and Distances between ARMA Models. Systems & Control Letters,2002,46(4): 265-270
[19]Gorelick L,Blank M,Shechtman E,Xet al. Actions as Space Time Shapes. IEEE Trans on Pattern Analysis and Machine Intelligence,2007,29(12): 2247-2253
[20]Schuldt C,Laptev I,Barbara C. Recognizing Human Actions: A Local SVM Approach // Proc of the 17th International Conference on Pattern Recognition. Washington,USA,2004: 32-36
[21]Cui Peng,Wang Fei,Sun Lifeng,Xet al. A Matrix Based Approach to Unsupervised Human Action Categorization. IEEE Trans on Multimedia,2012,14(1): 102-110
[22]Junejo I N,Dexter E,Laptev I,Xet al. View Independent Action Recognition from Temporal Self Similarities. IEEE Trans on Pattern Analysis and Machine Intelligence,2011,33(1): 172-185
[23]Ikizler N,Duygulu P. Histogram of Oriented Rectangles: A New Pose Descriptor for Human Action Recognition. Image and Vision Computing,2009,27(10): 1515-1526
[24]Kong Yu,Zhang Xiaoqin,Hu Weiming,Xet al. Adaptive Learning Codebook for Action Recognition. Pattern Recognition Letters,2011,32(8): 1178-1186
[25]Yang Wang,Greg M. Human Action Recognition by Semilatent Topic Models. IEEE Trans on Pattern Analysis and Machine Intelligence,2009,31(10): 1762-1774 |
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2013, Vol. 26 
