Human activity recognition in video using two methods for matching shape contexts of silhouettes

In this paper, activity recognition is performed based on silhouettes of the human figure obtained by background subtraction and characterized by the shape context, a log-polar histogram derived from boundary points. In the first approach each video frame is tagged by the activity corresponding to the closest matches between the query and known shapes. In the second method, the shape context dimensionality is reduced by principal components analysis, and a neural network is used for activity classification of individual frames. The overall decision for an entire video sequence is based on majority vote. Classification of individual frames ranged between 70-90% and overall classification of video sequences was very accurate.

[1]  Gang Xu,et al.  Understanding human motion patterns , 1994, Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 3 - Conference C: Signal Processing (Cat. No.94CH3440-5).

[2]  Feng Niu,et al.  View-invariant human activity recognition based on shape and motion features , 2004, IEEE Sixth International Symposium on Multimedia Software Engineering.

[3]  R. Venkatesh Babu,et al.  Recognition of human actions using motion history information extracted from the compressed video , 2004, Image Vis. Comput..

[4]  Osama Masoud,et al.  Recognizing human activities , 2003, Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance, 2003..

[5]  Alan M. McIvor,et al.  Background Subtraction Techniques , 2000 .

[6]  Jitendra Malik,et al.  Estimating Human Body Configurations Using Shape Context Matching , 2002, ECCV.

[7]  Hironobu Fujiyoshi,et al.  Real-time human motion analysis by image skeletonization , 1998, Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No.98EX201).

[8]  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.

[9]  Hsuan-Sheng Chen,et al.  Human action recognition using star skeleton , 2006, VSSN '06.

[10]  Sheng-Wen Shih,et al.  Continuous Human Action Segmentation and Recognition Using a Spatio-Temporal Probabilistic Framework , 2006, Eighth IEEE International Symposium on Multimedia (ISM'06).

[11]  Sudeep Sarkar,et al.  Challenges in Segmentation of Human Forms in Outdoor Video , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[12]  Alex Pentland,et al.  A Bayesian Computer Vision System for Modeling Human Interactions , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  H. Kuhn.  The Hungarian method for the assignment problem , 1955 .

[14]  Massimo Piccardi,et al.  Background subtraction techniques: a review , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[15]  Stefan Carlsson,et al.  Recognizing and Tracking Human Action , 2002, ECCV.

[16]  Matti Pietikäinen,et al.  Human Activity Recognition Using Sequences of Postures , 2005, MVA.

[17]  Jake K. Aggarwal,et al.  Segmentation and recognition of continuous human activity , 2001, Proceedings IEEE Workshop on Detection and Recognition of Events in Video.

[18]  Larry S. Davis,et al.  Non-parametric Model for Background Subtraction , 2000, ECCV.

[19]  Ronen Basri,et al.  Actions as Space-Time Shapes , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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