Action in chains: A chains model for action localization and classification

In this paper we present a method for action classification in videos using trajectory features. The novelty of our approach is in formulating the problem of simultaneous detection and localization as a probabilistic chains model. In our formulation, chains are sets of regions in the video that are connected based on their joint probabilities. We describe our approach for connecting subvolumes in the video into chains, and using them as spatio-temporal detectors for actions. Our approach allows the detection and localization of multiple actions occurring simultaneously or at different locations in a single video. We test the performance of our method on two challenging action recognition datasets, and compare to state of the art methods.

[1]  Cordelia Schmid,et al.  Action recognition by dense trajectories , 2011, CVPR 2011.

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

[3]  Shimon Ullman,et al.  The chains model for detecting parts by their context , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[4]  Yang Wang,et al.  Human Action Recognition by Semilatent Topic Models , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Trevor Darrell,et al.  The NBNN kernel , 2011, 2011 International Conference on Computer Vision.

[6]  Zicheng Liu,et al.  Hierarchical Filtered Motion for Action Recognition in Crowded Videos , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[7]  Iasonas Kokkinos,et al.  Discovering discriminative action parts from mid-level video representations , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Philip H. S. Torr,et al.  Learning discriminative space-time actions from weakly labelled videos , 2012, BMVC.

[9]  Bernt Schiele,et al.  A database for fine grained activity detection of cooking activities , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Zicheng Liu,et al.  Cross-dataset action detection , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[11]  Mohamed R. Amer,et al.  A chains model for localizing participants of group activities in videos , 2011, 2011 International Conference on Computer Vision.

[12]  C. Schmid,et al.  Recognizing activities with cluster-trees of tracklets , 2012, BMVC.

[13]  Ying Wu,et al.  Discriminative Video Pattern Search for Efficient Action Detection , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Eli Shechtman,et al.  In defense of Nearest-Neighbor based image classification , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.