Group motion segmentation using a Spatio-Temporal Driving Force Model

We consider the ‘group motion segmentation’ problem and provide a solution for it. The group motion segmentation problem aims at analyzing motion trajectories of multiple objects in video and finding among them the ones involved in a ‘group motion pattern’. This problem is motivated by and serves as the basis for the ‘multi-object activity recognition’ problem, which is currently an active research topic in event analysis and activity recognition. Specifically, we learn a Spatio-Temporal Driving Force Model to characterize a group motion pattern and design an approach for segmenting the group motion. We illustrate the approach using videos of American football plays, where we identify the offensive players, who follow an offensive motion pattern, from motions of all players in the field. Experiments using GaTech Football Play Dataset validate the effectiveness of the segmentation algorithm.

[1]  Mubarak Shah,et al.  Detecting group activities using rigidity of formation , 2005, MULTIMEDIA '05.

[2]  Jake K. Aggarwal,et al.  Spatio-temporal relationship match: Video structure comparison for recognition of complex human activities , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[3]  Eduardo Bayro-Corrochano,et al.  Lie algebra approach for tracking and 3D motion estimation using monocular vision , 2007, Image Vis. Comput..

[4]  Roberto Cipolla,et al.  Application of Lie Algebras to Visual Servoing , 2000, International Journal of Computer Vision.

[5]  Shuicheng Yan,et al.  Pair-activity classification by bi-trajectories analysis , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Dan Schonfeld,et al.  Event Analysis Based on Multiple Interactive Motion Trajectories , 2009, IEEE Transactions on Circuits and Systems for Video Technology.

[7]  Jing Xiao,et al.  A Closed-Form Solution to Non-Rigid Shape and Motion Recovery , 2004, International Journal of Computer Vision.

[8]  Shaogang Gong,et al.  Recognition of group activities using dynamic probabilistic networks , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[9]  Harold W. Kuhn,et al.  The Hungarian method for the assignment problem , 1955, 50 Years of Integer Programming.

[10]  C. W. Gear,et al.  Multibody Grouping from Motion Images , 1998, International Journal of Computer Vision.

[11]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[12]  S. Shankar Sastry,et al.  Generalized principal component analysis (GPCA) , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Marc Pollefeys,et al.  A General Framework for Motion Segmentation: Independent, Articulated, Rigid, Non-rigid, Degenerate and Non-degenerate , 2006, ECCV.

[14]  Luc Van Gool,et al.  Vision and Lie's approach to invariance , 1995, Image Vis. Comput..

[15]  René Vidal,et al.  A Unified Algebraic Approach to 2-D and 3-D Motion Segmentation , 2004, ECCV.

[16]  Rama Chellappa,et al.  "Shape Activity": a continuous-state HMM for moving/deforming shapes with application to abnormal activity detection , 2005, IEEE Transactions on Image Processing.

[17]  Bingbing Ni,et al.  Recognizing human group activities with localized causalities , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  W. Rossmann Lie Groups: An Introduction through Linear Groups , 2002 .

[19]  Kenichi Kanatani,et al.  Motion segmentation by subspace separation and model selection , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[20]  Ramakant Nevatia,et al.  Multi-agent event recognition , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[21]  Michael I. Jordan,et al.  Variational inference for Dirichlet process mixtures , 2006 .

[22]  Peter Meer,et al.  Simultaneous multiple 3D motion estimation via mode finding on Lie groups , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[23]  Rajesh P. N. Rao,et al.  Learning Lie Groups for Invariant Visual Perception , 1998, NIPS.

[24]  Rama Chellappa,et al.  A Multiple-Hypothesis Approach for Multiobject Visual Tracking , 2007, IEEE Transactions on Image Processing.

[25]  René Vidal,et al.  Motion segmentation via robust subspace separation in the presence of outlying, incomplete, or corrupted trajectories , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[26]  Xiaohui Liu,et al.  Multi-agent activity recognition using observation decomposedhidden Markov models , 2006, Image Vis. Comput..

[27]  Dahua Lin,et al.  Learning visual flows: A Lie algebraic approach , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[28]  Venu Madhav Govindu,et al.  Lie-algebraic averaging for globally consistent motion estimation , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[29]  Rama Chellappa,et al.  Learning multi-modal densities on Discriminative Temporal Interaction Manifold for group activity recognition , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[30]  René Vidal,et al.  A Unified Algebraic Approach to 2-D and 3-D Motion Segmentation and Estimation , 2006, Journal of Mathematical Imaging and Vision.

[31]  Bingbing Ni,et al.  Recognizing human group activities with localized causalities , 2009, CVPR 2009.

[32]  Lihi Zelnik-Manor,et al.  Degeneracies, dependencies and their implications in multi-body and multi-sequence factorizations , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[33]  Takeo Kanade,et al.  A Multibody Factorization Method for Independently Moving Objects , 1998, International Journal of Computer Vision.

[34]  Mubarak Shah,et al.  Learning, detection and representation of multi-agent events in videos , 2007, Artif. Intell..

[35]  Aaron F. Bobick,et al.  Recognizing Planned, Multiperson Action , 2001, Comput. Vis. Image Underst..

[36]  Anthony Hoogs,et al.  Learning and Recognizing American Football Plays , .

[37]  Naoyuki Ichimura Motion segmentation based on factorization method and discriminant criterion , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[38]  Matthew Brand,et al.  Morphable 3D models from video , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[39]  Henning Biermann,et al.  Recovering non-rigid 3D shape from image streams , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).