Video verification of point of sale transactions

Loss prevention is a significant challenge in retail enterprises. A significant percentage of this loss occurs at point of sale (POS) terminals. POS data mining tools known collectively as exception based reporting (EBR) are helping retailers, but they have limitations as they can only work statistically on trends and anomalies in digital POS data. By applying video analytics techniques to POS transactions, it is possible to detect fraudulent or anomalous activity at the level of individual transactions. Very specific fraudulent behaviors that cannot be detected via POS data alone become clear when combined with video-derived data. ObjectVideo, a provider of intelligent video software, has produced a system called RetailWatch that combines POS information with video data to create a unique loss prevention tool. This paper describes the system architecture, algorithmic approach, and capabilities of the system, together with a customer case-study illustrating the results and effectiveness of the system.

[1]  Zoran Zivkovic,et al.  Improved adaptive Gaussian mixture model for background subtraction , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[2]  Michael Isard,et al.  BraMBLe: a Bayesian multiple-blob tracker , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

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

[4]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[5]  Thomas Serre,et al.  Object recognition with features inspired by visual cortex , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[6]  Tomaso A. Poggio,et al.  Example-Based Object Detection in Images by Components , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Takeo Kanade,et al.  A statistical method for 3D object detection applied to faces and cars , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

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

[9]  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).

[10]  A. J. Lipton.  Keynote: intelligent video as a force multiplier for crime detection and prevention , 2005 .

[11]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[12]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[13]  Gérard G. Medioni,et al.  Detecting and tracking moving objects for video surveillance , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[14]  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).

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