Automated visual analysis in large scale sensor networks

Modern automated video analysis systems consist of large networks of heterogeneous sensors. These systems must extract, integrate and present relevant information from the sensors in real-time. This paper addresses some of the major challenges such systems face: efficient video processing for high-resolution sensors; data fusion across multiple modalities; robustness to changing environmental conditions and video processing errors; and intuitive user interfaces for visualization and analysis. The paper discusses enabling technologies to overcome these challenges and presents a case study of a wide area video analysis system deployed at a port in the state of Florida, USA. The components of the system are also detailed and justified using quantitative and qualitative results.

[1]  Kentaro Toyama,et al.  Wallflower: principles and practice of background maintenance , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

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

[3]  Omar Javed,et al.  Self Calibrating Visual Sensor Networks , 2008, 2008 IEEE Workshop on Applications of Computer Vision.

[4]  Mubarak Shah,et al.  Automated Visual Surveillance in Realistic Scenarios , 2007, IEEE MultiMedia.

[5]  Antonio Torralba,et al.  Contextual guidance of eye movements and attention in real-world scenes: the role of global features in object search. , 2006, Psychological review.

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

[7]  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).

[8]  Takeo Kanade,et al.  Algorithms for cooperative multisensor surveillance , 2001, Proc. IEEE.

[9]  Songhwai Oh,et al.  Markov chain Monte Carlo data association for general multiple-target tracking problems , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[10]  Andrea Cavallaro,et al.  Multi-Camera Scene Analysis using an Object-Centric Continuous Distribution Hidden Markov Model , 2007, 2007 IEEE International Conference on Image Processing.

[11]  PoggioTomaso,et al.  Robust Object Recognition with Cortex-Like Mechanisms , 2007 .

[12]  Alexei A. Efros,et al.  Putting Objects in Perspective , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[13]  J. Friedman.  Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .

[14]  Luca Benini,et al.  An integrated multi-modal sensor network for video surveillance , 2005, VSSN '05.

[15]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[16]  Mohan M. Trivedi,et al.  Editorial: Novel concepts and challenges for the next generation of video surveillance , 2007 .

[17]  C. Koch,et al.  Computational modelling of visual attention , 2001, Nature Reviews Neuroscience.

[18]  Alex Pentland,et al.  Pfinder: Real-Time Tracking of the Human Body , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Yanxi Liu,et al.  Online selection of discriminative tracking features , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Mun Wai Lee,et al.  Image transformation for object tracking in high-resolution video , 2008, 2008 19th International Conference on Pattern Recognition.

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

[22]  John A. Antoniades,et al.  Autonomous real-time ground ubiquitous surveillance-imaging system (ARGUS-IS) , 2008, SPIE Defense + Commercial Sensing.

[23]  Mubarak Shah,et al.  Probabilistic Modeling of Scene Dynamics for Applications in Visual Surveillance , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.