We Canwatch It For You Wholesale

This chapter provides an introduction to video analytics—a branch of computer vision technology that deals with automatic detection of activities and events in surveillance video feeds. Initial applications focused on the security and surveillance space, but as the technology improves it is rapidly finding a home in many other application areas. This chapter looks at some of those spaces, the requirements they impose on video analytics systems, and provides an example architecture and set of technology components to meet those requirements. This exemplary system is put through its paces to see how it stacks up in an embedded environment. Finally, we explore the future of video analytics and examine some of the market requirements that are driving breakthroughs in both video analytics and processor platform technology alike.

[1]  Larry S. Davis,et al.  Non-parametric Model for Background Subtraction , 2000, ECCV.

[2]  Dorin Comaniciu,et al.  Real-time tracking of non-rigid objects using mean shift , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[3]  Dana H. Ballard,et al.  Computer Vision , 1982 .

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

[5]  David G. Stork,et al.  Pattern Classification , 1973 .

[6]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[7]  Terrance E. Boult,et al.  Frame-rate omnidirectional surveillance and tracking of camouflaged and occluded targets , 1999, Proceedings Second IEEE Workshop on Visual Surveillance (VS'99) (Cat. No.98-89223).

[8]  Steven S. Beauchemin,et al.  The computation of optical flow , 1995, CSUR.

[9]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[10]  Sergio A. Velastin,et al.  PRISMATICA: toward ambient intelligence in public transport environments , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[11]  Seymour A. Papert,et al.  The Summer Vision Project , 1966 .

[12]  David G. Stork,et al.  Pattern classification, 2nd Edition , 2000 .

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

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

[15]  John Langford,et al.  Telling humans and computers apart automatically , 2004, CACM.

[16]  D. M. Hutton,et al.  The Essential Turing , 2007 .

[17]  J. Howard Johnson,et al.  Analysis of Image Forming Systems , 1985 .

[18]  P. L. Venetianer,et al.  Objectvideo forensics: activity-based video indexing and retrieval for physical security applications , 2004 .

[19]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[20]  A. M. Turing,et al.  Computing Machinery and Intelligence , 1950, The Philosophy of Artificial Intelligence.