Interactive Video Surveillance for Perimeter Control

This chapter presents an interactive video-surveillance solution for assisting human operators in the control of movements across a multi-region scenario (perimeter control). It has been conceived as a multi-camera system to detect anomalous trajectory events, such as entering or leaving a region or changing the walking speed, by means of a dynamic collection of decision rules. They relate spatio-temporal patterns and event categories (anomalous, unknown, normal), and are used to assess and classify trajectory events. The interactive paradigm has been adopted as a natural framework to progressively learn and update rules, particularly at early stages of the system operation. The approach of continuously improving system knowledge from user feedback conducts to adaptive, reliable and increasingly automatic systems in a relatively short period of time.

[1]  Juan Rosell,et al.  Embedded low-level video processing for surveillance purposes , 2010, 3rd International Conference on Human System Interaction.

[2]  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.

[3]  Andrea Cavallaro,et al.  Video Analytics for Surveillance: Theory and Practice [From the Guest Editors] , 2010 .

[4]  Sergio A. Velastin,et al.  Intelligent distributed surveillance systems: a review , 2005 .

[5]  M. Thomason Interactive Pattern Recognition , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Nikos Paragios,et al.  Video-Based Surveillance Systems , 2002, Springer US.

[7]  Larry S. Davis,et al.  W4: Real-Time Surveillance of People and Their Activities , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Issa Traore,et al.  Converting Declarative Rules into Decision Trees , 2009 .

[9]  Takeo Kanade,et al.  A System for Video Surveillance and Monitoring , 2000 .

[10]  Pawel Forczmanski,et al.  SmartMonitor: An Approach to Simple, Intelligent and Affordable Visual Surveillance System , 2012, ICCVG.

[11]  Andrei Popescu-Belis,et al.  Machine Learning for Multimodal Interaction , 4th International Workshop, MLMI 2007, Brno, Czech Republic, June 28-30, 2007, Revised Selected Papers , 2008, MLMI.

[12]  Bernhard Rinner,et al.  Distributed embedded smart cameras for surveillance applications , 2006, Computer.

[13]  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).

[14]  Azriel Rosenfeld,et al.  Tracking Groups of People , 2000, Comput. Vis. Image Underst..

[15]  Ryszard Tadeusiewicz,et al.  Computer Vision and Graphics , 2014, Lecture Notes in Computer Science.

[16]  Yi-tzuu T. Chien Interactive pattern recognition , 1978 .

[17]  Vassilios Morellas,et al.  Two Examples of Indoor and Outdoor Surveillance Systems: Motivation, Design, and Testing , 2002 .