Human Action Recognition by Radon Transform

A new feature description is used for human behaviour representation and recognition. The feature is based on Radon transforms of extracted silhouettes. Key postures are selected based on the Radon transform. Key postures are combined to construct an action template for each sequence. Linear discriminant analysis (LDA) is applied to the set of key postures to obtain low dimensional feature vectors. Different classification methods are used to classify each sequence. Experiments are carried out based on a publically available human behaviour database and the results are exciting.

[1]  Jitendra Malik,et al.  Recognizing action at a distance , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[2]  S. Deans.  The Radon Transform and Some of Its Applications , 1983 .

[3]  Bir Bhanu,et al.  Individual recognition using gait energy image , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[5]  M. Mandal,et al.  Pose recognition using the Radon transform , 2005, 48th Midwest Symposium on Circuits and Systems, 2005..

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

[7]  Chia-Wen Lin,et al.  Automatic Key Posture Selection for Human Behavior Analysis , 2005, 2005 IEEE 7th Workshop on Multimedia Signal Processing.

[8]  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.

[9]  Andrew Blake,et al.  Probabilistic Tracking with Exemplars in a Metric Space , 2002, International Journal of Computer Vision.

[10]  Aaron E. Rosenberg,et al.  Performance tradeoffs in dynamic time warping algorithms for isolated word recognition , 1980 .

[11]  Daniel Thalmann,et al.  Key-posture extraction out of human motion data , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[12]  Janko Calic,et al.  Efficient key-frame extraction and video analysis , 2002, Proceedings. International Conference on Information Technology: Coding and Computing.

[13]  D. Hatzinakos,et al.  Gait recognition: a challenging signal processing technology for biometric identification , 2005, IEEE Signal Processing Magazine.

[14]  Yang Song,et al.  Unsupervised Learning of Human Motion , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

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

[16]  Delbert Dueck,et al.  Clustering by Passing Messages Between Data Points , 2007, Science.

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