Human Action Recognition Based on Global Silhouette and Local Optical Flow

Currently, Human Activity Recognition is a research hotspot in the field of machine vision, it involves knowledge of image processing, pattern recognition, artificial intelligence and many other disciplines. Video-based Human Activity Recognition including human area detection, movement and gesture segmentation, objective analysis and behavior understands for activity recognition and so on. In the past, the behavior recognition technology based on the single characteristic was too restrictive, in this paper, we proposed a mixed feature which combined global silhouette feature and local optical flow feature, and this combined representation was used for human action recognition. In the end, test the model with other samples from the database. Keywords— Action recognition; Computer vision; Global silhouette; Local optical

[1]  Kanad K. Biswas,et al.  Human Action Recognition Based on Spatio-temporal Features , 2009, PReMI.

[2]  Thomas B. Moeslund,et al.  A Survey of Computer Vision-Based Human Motion Capture , 2001, Comput. Vis. Image Underst..

[3]  J.K. Aggarwal,et al.  Human activity analysis , 2011, ACM Comput. Surv..

[4]  Edmond Boyer,et al.  Action recognition using exemplar-based embedding , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  F BobickAaron,et al.  The Recognition of Human Movement Using Temporal Templates , 2001 .

[6]  Dariu Gavrila,et al.  The Visual Analysis of Human Movement: A Survey , 1999, Comput. Vis. Image Underst..

[7]  Ronald Poppe,et al.  A survey on vision-based human action recognition , 2010, Image Vis. Comput..

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

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

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

[11]  Adrian Hilton,et al.  A survey of advances in vision-based human motion capture and analysis , 2006, Comput. Vis. Image Underst..

[12]  Rama Chellappa,et al.  Machine Recognition of Human Activities: A Survey , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[13]  Du Tran,et al.  Human Activity Recognition with Metric Learning , 2008, ECCV.

[14]  Ying Wang,et al.  Human Activity Recognition Based on R Transform , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Jake K. Aggarwal,et al.  Human Motion Analysis: A Review , 1999, Comput. Vis. Image Underst..

[16]  James W. Davis,et al.  The Recognition of Human Movement Using Temporal Templates , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

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