Determining activity patterns in retail spaces through video analysis

ABSTRACT

Retail establishments want to know about traffic flow and patterns of activity in order to better arrange and staff their business. A large number of fixed video cameras are commonly installed at these locations. While they can be used to observe activity in the retail environment, assigning personnel to this is too time consuming to be valuable for retail analysis. We have developed video processing and visualization techniques that generate presentations appropriate for examining traffic flow and changes in activity at different times of the day. Taking the results of video tracking software as input, our system aggregates activity in different regions of the area being analyzed, determines the average speed of moving objects in the region, and segments time based on significant changes in the quantity and/or location of activity. Visualizations present the results as heat maps to show activity and object counts and average velocities overlaid on the map of the space.

REFERENCES

• 1: D. Demirdjian , K. Tollmar , K. Koile , N. Checka , T. Darrell, Activity maps for location-aware computing, Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision, p.70, December 03-04, 2002
• 2: Andreas Girgensohn , Don Kimber , Jim Vaughan , Tao Yang , Frank Shipman , Thea Turner , Eleanor Rieffel , Lynn Wilcox , Francine Chen , Tony Dunnigan, DOTS: support for effective video surveillance, Proceedings of the 15th international conference on Multimedia, September 25-29, 2007, Augsburg, Germany [doi>10.1145/1291233.1291332]
• 3: Larson, J., Bradlow, E., and Fader, P. "An Exploratory Look at In-Store Supermarket Shopping Patterns", Wharton School of Business, University of Pennsylvania.
• 4: Gopal Pingali , Agata Opalach , Ingrid Carlbom, Multimedia retrieval through spatio-temporal activity maps, Proceedings of the ninth ACM international conference on Multimedia, September 30-October 05, 2001, Ottawa, Canada [doi>10.1145/500141.500162]
• 5: Porikli, F. "Multi-Camera Surveillance: Object-Based Summarization Approach", 2003. http://www.merl.com/papers/docs/TR2003-145.pdf
• 6: Santini, S. "Analysis of traffic flow in urban areas using web cameras", Proceedings of IEEE Workshop on Applications of Computer Vision, 2000.
• 7: Chris Stauffer , W. Eric L. Grimson, Learning Patterns of Activity Using Real-Time Tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.22 n.8, p.747-757, August 2000 [doi>10.1109/34.868677]
• 9: Tag Team: Tracking the Patterns of Supermarket Shoppers", Knowledge@Wharton, http://knowledge.wharton.upenn.edu, 2005.
• 10: Xiang, T. and Gong, S. "Activity Based Video Content Trajectory Representation and Segmentation", Proceedings of BMVC, 2004, pp. 177--186.
• 11: Yang, T., Chen, F., Kimber, D., and Vaughan, J. Robust People Detection and Tracking in a Multi-camera Indoor Visual Surveillance System. ICME 2007, pp. 675--678, 2007.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.1 Multimedia Information Systems
          Subjects: Video (e.g., tape, disk, DVI)

General Terms:
Algorithms, Design, Human Factors

Keywords:
activity segmentation, multiple security cameras, retail analysis, video surveillance