Exploiting Temporal Statistics for Events Analysis and Understanding

In this paper, we propose a technique for detecting possible anomalous events in an area monitored by a video surveillance system. In particular, here we focus on the time spent by an object to carry out simple events. Mixtures of Gaussians are maintained for each event to have a statistical representation of the times commonly required to perform certain activities. Such statistics are then exploited both for the analysis of the simple activities and for discovering anomalous situations (i.e. complex events). In these cases the system requires the attention of the human operator. Experiments have been performed on a multi-camera system for parking lot security.

[1]  Ramakant Nevatia,et al.  VERL: An Ontology Framework for Representing and Annotating Video Events , 2005, IEEE Multim..

[2]  M. Thonnat,et al.  Video sequence interpretation for visual surveillance , 2000, Proceedings Third IEEE International Workshop on Visual Surveillance.

[3]  Sei-Wang Chen,et al.  Automatic license plate recognition , 2004, IEEE Transactions on Intelligent Transportation Systems.

[4]  Fatih Murat Porikli,et al.  Event Detection by Eigenvector Decomposition Using Object and Frame Features , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[5]  Gian Luca Foresti,et al.  An adaptive high-order neural tree for pattern recognition , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[6]  Christophe Dousson,et al.  Chronicle Recognition Improvement Using Temporal Focusing and Hierarchization , 2007, IJCAI.

[7]  Gian Luca Foresti,et al.  Trajectory clustering and its applications for video surveillance , 2005, IEEE Conference on Advanced Video and Signal Based Surveillance, 2005..

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

[9]  Larry S. Davis,et al.  VidMAP: video monitoring of activity with Prolog , 2005, IEEE Conference on Advanced Video and Signal Based Surveillance, 2005..

[10]  Gian Luca Foresti,et al.  Activity Analysis for Video Security Systems , 2006, 2006 International Conference on Image Processing.

[11]  Gian Luca Foresti,et al.  Active Video-Based Surveillance System , 2005 .

[12]  Dorin Comaniciu,et al.  Kernel-Based Object Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Gian Luca Foresti,et al.  Event classification for automatic visual-based surveillance of parking lots , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[14]  Mario Vento,et al.  Thirty Years Of Graph Matching In Pattern Recognition , 2004, Int. J. Pattern Recognit. Artif. Intell..

[15]  G.L. Foresti,et al.  Active video-based surveillance system: the low-level image and video processing techniques needed for implementation , 2005, IEEE Signal Processing Magazine.

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

[17]  Gian Luca Foresti,et al.  Adaptive high order neural trees for pattern recognition , 2002, Object recognition supported by user interaction for service robots.

[18]  Michael Spann,et al.  Event detection for intelligent car park video surveillance , 2005, Real Time Imaging.

[19]  Gian Luca Foresti,et al.  On-line trajectory clustering for anomalous events detection , 2006, Pattern Recognit. Lett..

[20]  Shengrui Wang,et al.  A new algorithm for inexact graph matching , 2002, Object recognition supported by user interaction for service robots.

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

[23]  Roberto Marcondes Cesar Junior,et al.  Inexact graph matching for model-based recognition: Evaluation and comparison of optimization algorithms , 2005, Pattern Recognit..

[24]  Terry Caelli,et al.  An eigenspace projection clustering method for inexact graph matching , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.