Multi-view indoor human behavior recognition based on 3D skeleton

For the problems caused by viewpoint changes in activity recognition, a multi-view interior human behavior recognition method based on 3D framework is presented. First, Microsoft's Kinect device is used to obtain body motion video in the positive perspective, the oblique angle and the side perspective. Second, it extracts bone joints and get global human features and the local features of arms and legs at the same time to form 3D skeletal features set. Third, online dictionary learning on feature set is used to reduce the dimension of feature. Finally, linear support vector machine (LSVM) is used to obtain the results of behavior recognition. The experimental results show that this method has better recognition rate.

[1]  Hassan Foroosh,et al.  Action recognition based on homography constraints , 2008, 2008 19th International Conference on Pattern Recognition.

[2]  Guangyou Xu,et al.  Human action recognition with primitive-based coupled-HMM , 2002, Object recognition supported by user interaction for service robots.

[3]  Guillermo Sapiro,et al.  Online Learning for Matrix Factorization and Sparse Coding , 2009, J. Mach. Learn. Res..

[4]  G. Johansson.  Visual motion perception. , 1975, Scientific American.

[5]  Liang Wang,et al.  Informative Shape Representations for Human Action Recognition , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[6]  Mohiuddin Ahmad,et al.  Human action recognition using shape and CLG-motion flow from multi-view image sequences , 2008, Pattern Recognit..

[7]  Rémi Ronfard,et al.  Action Recognition from Arbitrary Views using 3D Exemplars , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[8]  Toby Sharp,et al.  Real-time human pose recognition in parts from single depth images , 2011, CVPR.

[9]  James W. Davis,et al.  The representation and recognition of human movement using temporal templates , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  Mubarak Shah,et al.  Matching actions in presence of camera motion , 2006, Comput. Vis. Image Underst..

[11]  Ali Farhadi,et al.  Learning to Recognize Activities from the Wrong View Point , 2008, ECCV.

[12]  Krystian Mikolajczyk,et al.  Feature Tracking and Motion Compensation for Action Recognition , 2008, BMVC.