Towards Detection of Suspicious Behavior from Multiple Observations

This paper addresses the problem of detecting suspicious behavior from a collection of individuals events, where no single event is enough to decide whether his/her behavior is suspicious, but the combination of multiple events enables reasoning. We establish a Bayesian framework for evaluating multiple events and show that the current approaches lack modeling behavior history included in the estimation whether a trace of events is generated by a suspicious agent. We propose a heuristic for evaluating events according to the behavior of the agent in the past. The proposed approach, tested on an airport domain, outperforms the current approaches.

[1]  Ramakant Nevatia,et al.  Large-scale event detection using semi-hidden Markov models , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[2]  Craig W. Reynolds.  Steering Behaviors For Autonomous Characters , 1999 .

[3]  Maurizio Filippone,et al.  Detecting Suspicious Behavior in Surveillance Images , 2008, 2008 IEEE International Conference on Data Mining Workshops.

[4]  Svetha Venkatesh,et al.  Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[5]  Yoram Singer,et al.  The Hierarchical Hidden Markov Model: Analysis and Applications , 1998, Machine Learning.

[6]  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.

[7]  Daniel A. Keim,et al.  Integrative visual analytics for suspicious behavior detection , 2009, 2009 IEEE Symposium on Visual Analytics Science and Technology.

[8]  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.

[9]  Svetha Venkatesh,et al.  Activity recognition and abnormality detection with the switching hidden semi-Markov model , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[10]  Gal A. Kaminka,et al.  Incorporating Observer Biases in Keyhole Plan Recognition (Efficiently!) , 2007, AAAI.

[11]  Jianbo Shi,et al.  Detecting unusual activity in video , 2004, CVPR 2004.

[12]  L. Connors,et al.  The Washington Post , 2003 .

[13]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[14]  Björn W. Schuller,et al.  Suspicious Behavior Detection in Public Transport by Fusion of Low-Level Video Descriptors , 2007, 2007 IEEE International Conference on Multimedia and Expo.

[15]  Milind Tambe,et al.  ESCAPES: evacuation simulation with children, authorities, parents, emotions, and social comparison , 2011, AAMAS.

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

[17]  Paul Helman,et al.  Foundations of Intrusion Detection. , 1992 .

[18]  David A. Clausi,et al.  Goal-based trajectory analysis for unusual behaviour detection in intelligent surveillance , 2011, Image Vis. Comput..

[19]  Robert B. Fisher,et al.  Semi-supervised Learning for Anomalous Trajectory Detection , 2008, BMVC.

[20]  Gian Luca Foresti,et al.  Trajectory-Based Anomalous Event Detection , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[21]  Lisa M. Brown,et al.  Case Study: IBM Smart Surveillance System , 2009 .