Human action recognition with line and flow histograms

We present a compact representation for human action recognition in videos using line and optical flow histograms. We introduce a new shape descriptor based on the distribution of lines which are fitted to boundaries of human figures. By using an entropy-based approach, we apply feature selection to densify our feature representation, thus, minimizing classification time without degrading accuracy. We also use a compact representation of optical flow for motion information. Using line and flow histograms together with global velocity information, we show that high-accuracy action recognition is possible, even in challenging recording conditions.

[1]  Randal C. Nelson,et al.  Detecting activities , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Alex Pentland,et al.  Coupled hidden Markov models for complex action recognition , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  Dariu Gavrila,et al.  The Visual Analysis of Human Movement: A Survey , 1999, Comput. Vis. Image Underst..

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

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

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

[7]  Ramakant Nevatia,et al.  Video-based event recognition: activity representation and probabilistic recognition methods , 2004, Comput. Vis. Image Underst..

[8]  Jitendra Malik,et al.  Learning to detect natural image boundaries using local brightness, color, and texture cues , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[10]  Cristian Sminchisescu,et al.  Conditional Random Fields for Contextual Human Motion Recognition , 2005, ICCV.

[11]  Serge J. Belongie,et al.  Behavior recognition via sparse spatio-temporal features , 2005, 2005 IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance.

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

[13]  Thomas Serre,et al.  A Biologically Inspired System for Action Recognition , 2007, 2007 IEEE 11th International Conference on Computer Vision.

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

[15]  Martial Hebert,et al.  Spatio-temporal Shape and Flow Correlation for Action Recognition , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Pinar Duygulu Sahin,et al.  Human Action Recognition Using Distribution of Oriented Rectangular Patches , 2007, Workshop on Human Motion.

[17]  David A. Forsyth,et al.  Searching Video for Complex Activities with Finite State Models , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Tae-Kyun Kim,et al.  Learning Motion Categories using both Semantic and Structural Information , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

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