Detection and tracking of shopping groups in stores

We describe a monocular real-time computer vision system that identifies shopping groups by detecting and tracking multiple people as they wait in a checkout line or service counter. Our system segments each frame into foreground regions which contains multiple people. Foreground regions are further segmented into individuals using a temporal segmentation of foreground and motion cues. Once a person is detected, an appearance model based on color and edge density in conjunction with a mean-shift tracker is used to recover the person's trajectory. People are grouped together as a shopping group by analyzing interbody distances. The system also monitors the cashier's activities to determine when shopping transactions start and end. Experimental results demonstrate the robustness and real-time performance of the algorithm.

[1]  Dariu Gavrila,et al.  The Visual Analysis of Human Movement: A Survey , 1999, Comput. Vis. Image Underst..

[2]  Alex Pentland,et al.  Pfinder: real-time tracking of the human body , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

[3]  Rómer Rosales,et al.  3D trajectory recovery for tracking multiple objects and trajectory guided recognition of actions , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

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

[5]  James M. Rehg,et al.  Vision for a smart kiosk , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

[7]  Larry S. Davis,et al.  A Robust Background Subtraction and Shadow Detection , 1999 .

[8]  James W. Davis,et al.  Real-time recognition of activity using temporal templates , 1996, Proceedings Third IEEE Workshop on Applications of Computer Vision. WACV'96.

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

[10]  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).

[11]  Larry S. Davis,et al.  Hydra: multiple people detection and tracking using silhouettes , 1999, Proceedings 10th International Conference on Image Analysis and Processing.

[12]  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).

[13]  Trevor Darrell,et al.  Integrated Person Tracking Using Stereo, Color, and Pattern Detection , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).