Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multimedia retrieval through spatio-temporal activity maps
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Activity maps for location-aware computing
WACV '02 Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision
DOTS: support for effective video surveillance
Proceedings of the 15th international conference on Multimedia
Supporting the supermarket shopping experience through a context-aware shopping trolley
OZCHI '09 Proceedings of the 21st Annual Conference of the Australian Computer-Human Interaction Special Interest Group: Design: Open 24/7
Deriving implicit indoor scene structure with path analysis
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness
weShop: using social data as context in the retail experience
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
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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.