Silhouette-Edge-Based Descriptor for Human Action Representation and Recognition

Extraction and representation of postures and/or gestures from human activities in videos have been a focus of research in this area of action recognition. With various applications cropping up from different fields, this paper seeks to improve the performance of these action recognition machines by proposing a shape-based silhouette-edge descriptor for the human body. Information entropy, a method to measure the randomness of a sequence of symbols, is used to aid the selection of vital key postures from video frames. Morphological operations are applied to extract and stack edges to uniquely represent different actions shape-wise. To classify an action from a new input video, a Hausdorff distance measure is applied between the gallery representations and the query images formed from the proposed procedure. The method is tested on known public databases for its validation. An effective method of human action annotation and description has been effectively achieved.

[1]  Anil K. Jain,et al.  A modified Hausdorff distance for object matching , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[2]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[3]  Rafael C. González,et al.  Digital image processing using MATLAB , 2006 .

[4]  Ramakant Nevatia,et al.  Single View Human Action Recognition using Key Pose Matching and Viterbi Path Searching , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Ankur Agarwal,et al.  3D human pose from silhouettes by relevance vector regression , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[6]  Serge J. Belongie,et al.  Behavior recognition via sparse spatio-temporal features , 2005, 2005 IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance.

[7]  Pinar Duygulu Sahin,et al.  Histogram of oriented rectangles: A new pose descriptor for human action recognition , 2009, Image Vis. Comput..

[8]  Yan Ke,et al.  PCA-SIFT: a more distinctive representation for local image descriptors , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[9]  Miao Zhang,et al.  Accelerated Generation Algorithm for an Elemental Image Array Using Depth Information in Computational Integral Imaging , 2013, J. Inform. and Commun. Convergence Engineering.

[10]  Dong-Gyu Sim,et al.  Object matching algorithms using robust Hausdorff distance measures , 1999, IEEE Trans. Image Process..

[11]  Michael J. Black.  Explaining optical flow events with parameterized spatio-temporal models , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[12]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[13]  Ana Doblas,et al.  Shaded-Mask Filtering for Extended Depth-of-Field Microscopy , 2013, J. Inform. and Commun. Convergence Engineering.

[14]  Jitendra Malik,et al.  Shape matching and object recognition using shape contexts , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[15]  Thomas S. Huang,et al.  Facial expression recognition: A clustering-based approach , 2003, Pattern Recognit. Lett..

[16]  Ronen Basri,et al.  Actions as space-time shapes , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[17]  Daniel P. Huttenlocher,et al.  Comparing Images Using the Hausdorff Distance , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Geum-Boon Lee,et al.  Extraction of key postures using shape contexts , 2009, 2009 11th International Conference on Advanced Communication Technology.