Temporal Relations in Videos for Unsupervised Activity Analysis

Observing the different video sequences in Fig. 1, increments between frames are quite small compared to the changes throughout the whole sequence. For instance, the behavior of a tracked person (2nd row) is composed of a certain repertoire of activities with transitions in between that are typically short in comparison. This can also be observed at larger scales, like day-night changes or seasonal changes (3rd and 4th row) and already suggests a hierarchical structure.

[1]  Tieniu Tan,et al.  A survey on visual surveillance of object motion and behaviors , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[2]  Daphna Weinshall,et al.  Beyond Novelty Detection: Incongruent Events, When General and Specific Classifiers Disagree , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Terrence J. Sejnowski,et al.  Slow Feature Analysis: Unsupervised Learning of Invariances , 2002, Neural Computation.

[4]  Ehud Rivlin,et al.  Robust Real-Time Unusual Event Detection using Multiple Fixed-Location Monitors , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Fernando De la Torre,et al.  Unsupervised discovery of facial events , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[6]  Luc Van Gool,et al.  Automatic Workflow Monitoring in Industrial Environments , 2010, ACCV.

[7]  Shaogang Gong,et al.  A Markov Clustering Topic Model for mining behaviour in video , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[8]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[9]  Rama Chellappa,et al.  Unsupervised view and rate invariant clustering of video sequences q , 2009 .

[10]  W. Eric L. Grimson,et al.  Learning Patterns of Activity Using Real-Time Tracking , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Luc Van Gool,et al.  Hunting Nessie - Real-time abnormality detection from webcams , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[12]  J. Lange,et al.  A Model of Biological Motion Perception from Configural Form Cues , 2006, The Journal of Neuroscience.

[13]  Adrian Hilton,et al.  A survey of advances in vision-based human motion capture and analysis , 2006, Comput. Vis. Image Underst..

[14]  Tieniu Tan,et al.  A system for learning statistical motion patterns , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Luc Van Gool,et al.  Exploiting simple hierarchies for unsupervised human behavior analysis , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[16]  Luc Van Gool,et al.  What's going on? Discovering spatio-temporal dependencies in dynamic scenes , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[17]  Wolfgang Maass,et al.  Replacing supervised classification learning by Slow Feature Analysis in spiking neural networks , 2009, NIPS.

[18]  Juan Carlos Niebles,et al.  Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words , 2006, BMVC.

[19]  David J. Fleet,et al.  Temporal motion models for monocular and multiview 3D human body tracking , 2006, Comput. Vis. Image Underst..

[20]  W. Eric L. Grimson,et al.  Unsupervised Activity Perception in Crowded and Complicated Scenes Using Hierarchical Bayesian Models , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Juan Carlos Niebles,et al.  Modeling Temporal Structure of Decomposable Motion Segments for Activity Classification , 2010, ECCV.

[22]  Laurenz Wiskott,et al.  Slow Feature Analysis: A Theoretical Analysis of Optimal Free Responses , 2003, Neural Computation.