Temporal Structure Models for Event Recognition

In many areas of visual surveillance, the observed activity follows re-occurring patterns. This paper demonstrates how such patterns can be exploited to improve the detection rate of independent event detectors. We present a temporal model based on pairwise correlations between event timings, which efficiently exploits limited training data. This is combined with the response from potentially heterogeneous independent event detectors to improve the robustness of detections over extended sequences. We demonstrate the efficacy of our system with rigorous testing on a large real-world dataset of aircraft servicing operations. We describe the implementation of a binary classifier based on local histograms of optical flow which is used as the independent event detector in our experiments.

[1]  Martial Hebert,et al.  Representing Pairwise Spatial and Temporal Relations for Action Recognition , 2010, ECCV.

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

[3]  Carl E. Rasmussen,et al.  The Infinite Gaussian Mixture Model , 1999, NIPS.

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

[5]  Jean Ponce,et al.  Automatic annotation of human actions in video , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[6]  M. Birkner,et al.  Blow-up of semilinear PDE's at the critical dimension. A probabilistic approach , 2002 .

[7]  Thomas S. Huang,et al.  CVIU special issue on event detection in video , 2004, Comput. Vis. Image Underst..

[8]  Eric Horvitz,et al.  Layered representations for learning and inferring office activity from multiple sensory channels , 2004, Comput. Vis. Image Underst..

[9]  François Brémond,et al.  Automatic Video Interpretation: A Novel Algorithm for Temporal Scenario Recognition , 2003, IJCAI.

[10]  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.

[11]  Daniel P. Huttenlocher,et al.  Pictorial Structures for Object Recognition , 2004, International Journal of Computer Vision.

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

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

[14]  Aaron F. Bobick,et al.  Recognition of Visual Activities and Interactions by Stochastic Parsing , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[16]  S. Gong,et al.  Recognising action as clouds of space-time interest points , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

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

[19]  J. Kruskal.  On the shortest spanning subtree of a graph and the traveling salesman problem , 1956 .

[20]  Shaogang Gong,et al.  Beyond Tracking: Modelling Activity and Understanding Behaviour , 2006, International Journal of Computer Vision.

[21]  Cordelia Schmid,et al.  Evaluation of Local Spatio-temporal Features for Action Recognition , 2009, BMVC.

[22]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[23]  Barbara Caputo,et al.  Local velocity-adapted motion events for spatio-temporal recognition , 2007, Comput. Vis. Image Underst..

[24]  Cordelia Schmid,et al.  Learning realistic human actions from movies , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.