Detection and Tracking of Shopping Groups in Stores
Abstract
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.
Cite
Text
Haritaoglu and Flickner. "Detection and Tracking of Shopping Groups in Stores." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2001. doi:10.1109/CVPR.2001.990507Markdown
[Haritaoglu and Flickner. "Detection and Tracking of Shopping Groups in Stores." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2001.](https://mlanthology.org/cvpr/2001/haritaoglu2001cvpr-detection/) doi:10.1109/CVPR.2001.990507BibTeX
@inproceedings{haritaoglu2001cvpr-detection,
title = {{Detection and Tracking of Shopping Groups in Stores}},
author = {Haritaoglu, Ismail and Flickner, Myron},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2001},
pages = {I:431-438},
doi = {10.1109/CVPR.2001.990507},
url = {https://mlanthology.org/cvpr/2001/haritaoglu2001cvpr-detection/}
}