Non-negative sparse coding for human action recognition

We consider the problem of human action recognition using non-negative sparse representation of extracted features from spatiotemporal video patches. Our algorithm trains dictionaries for the calculation of a non-negative sparse representation for feature vectors and uses a linear Support Vector Machine (SVM) to distinguish between different actions. We evaluate the performance of the proposed techniques by using two human action datasets (KTH and IXMAS). In both cases, the proposed technique outperforms state-of-the-art techniques, achieving 100% accuracy on the KTH dataset.

[1]  Chih-Jen Lin,et al.  LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..

[2]  Yihong Gong,et al.  Linear spatial pyramid matching using sparse coding for image classification , 2009, CVPR.

[3]  Patrick Pérez,et al.  View-Independent Action Recognition from Temporal Self-Similarities , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Shaogang Gong,et al.  Recognising action as clouds of space-time interest points , 2009, CVPR.

[5]  R. Tibshirani,et al.  Least angle regression , 2004, math/0406456.

[6]  Ivan Laptev,et al.  On Space-Time Interest Points , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[7]  Ali Farhadi,et al.  Learning to Recognize Activities from the Wrong View Point , 2008, ECCV.

[8]  Julien Mairal,et al.  Convex optimization with sparsity-inducing norms , 2011 .

[9]  Rajat Raina,et al.  Efficient sparse coding algorithms , 2006, NIPS.

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

[11]  Cordelia Schmid,et al.  Evaluation of Local Spatio-temporal Features for Action Recognition , 2009, BMVC.

[12]  Cordelia Schmid,et al.  Learning realistic human actions from movies , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Rémi Ronfard,et al.  Free viewpoint action recognition using motion history volumes , 2006, Comput. Vis. Image Underst..

[14]  Greg Mori,et al.  Action recognition by learning mid-level motion features , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Yun Fu,et al.  Sparse Coding on Local Spatial-Temporal Volumes for Human Action Recognition , 2010, ACCV.

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

[17]  M. Alex O. Vasilescu,et al.  Recognizing action events from multiple viewpoints , 2001, Proceedings IEEE Workshop on Detection and Recognition of Events in Video.

[18]  Barbara Caputo,et al.  Recognizing human actions: a local SVM approach , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[19]  Adriana Kovashka,et al.  Learning a hierarchy of discriminative space-time neighborhood features for human action recognition , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[20]  Le Li,et al.  SENSC: a Stable and Efficient Algorithm for Nonnegative Sparse Coding: SENSC: a Stable and Efficient Algorithm for Nonnegative Sparse Coding , 2009 .

[21]  Jean Ponce,et al.  Learning mid-level features for recognition , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[22]  Guillermo Sapiro,et al.  Online Learning for Matrix Factorization and Sparse Coding , 2009, J. Mach. Learn. Res..