Efficient human action recognition by luminance field trajectory and geometry information

In recent years the video event understanding is an active research topic, with many applications in surveillance, security, and multimedia search and mining. In this paper we focus on the human action recognition problem and propose a new Curve-Distance approach based on the geometry modeling of video appearance manifold and the human action time series statistics on the geometry information. Experimental results on the KTH database demonstrate the solution to be effective and promising.

[1]  Zhu Li,et al.  Real-time human action recognition by luminance field trajectory analysis , 2008, ACM Multimedia.

[2]  Shih-Fu Chang,et al.  A fully automated content-based video search engine supporting spatiotemporal queries , 1998, IEEE Trans. Circuits Syst. Video Technol..

[3]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[4]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

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

[6]  Tae-Kyun Kim,et al.  Tensor Canonical Correlation Analysis for Action Classification , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

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

[8]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[9]  Martial Hebert,et al.  Efficient visual event detection using volumetric features , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[10]  Aggelos K. Katsaggelos,et al.  Locally Embedded Linear Subspaces for Efficient Video Indexing and Retrieval , 2006, 2006 IEEE International Conference on Multimedia and Expo.

[11]  Juan Carlos Niebles,et al.  Unsupervised Learning of Human Action Categories , 2006 .

[12]  Aggelos K. Katsaggelos,et al.  Locally adaptive subspace and similarity metric learning for visual data clustering and retrieval , 2008, Comput. Vis. Image Underst..