Human Action Recognition Based on Template Matching

Abstract This paper presents a new method of human action recognition, which is based on ℜ transform and template matching after the key frame is extracted from a cycle. For a key binary human silhouette, ℜ transform is employed to represent low-level features. The advantage of the ℜ transform lies in its low computational complexity and geometric invariance. We utilize a novel string matching scheme based on edit distance is proposed to analyze different human actions. Compared with other methods, ours is superior because the descriptor is robust to frame loss in the video sequence, disjoint silhouettes and holes in the shape, and thus achieves better performance in similar activities recognition, simple representation, computational complexity and template generalization. Sufficient experiments have proved the efficiency.

[1]  Maja Pantic,et al.  B-spline polynomial descriptors for human activity recognition , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[2]  Shih-Fu Chang,et al.  Image Retrieval: Current Techniques, Promising Directions, and Open Issues , 1999, J. Vis. Commun. Image Represent..

[3]  Laurent Wendling,et al.  A new shape descriptor defined on the Radon transform , 2006, Comput. Vis. Image Underst..

[4]  Rama Chellappa,et al.  "Shape Activity": a continuous-state HMM for moving/deforming shapes with application to abnormal activity detection , 2005, IEEE Transactions on Image Processing.

[5]  Filiberto Pla,et al.  Extracting Motion Features for Visual Human Activity Representation , 2005, IbPRIA.

[6]  Qiang Wu,et al.  Using dynamic programming to match human behavior sequences , 2008, 2008 10th International Conference on Control, Automation, Robotics and Vision.

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

[8]  Vladimir I. Levenshtein,et al.  Binary codes capable of correcting deletions, insertions, and reversals , 1965 .

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

[10]  Sudeep Sarkar,et al.  The humanID gait challenge problem: data sets, performance, and analysis , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Stefan Carlsson,et al.  Pose-based clustering in action sequences , 2003, First IEEE International Workshop on Higher-Level Knowledge in 3D Modeling and Motion Analysis, 2003. HLK 2003..

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