Computer vision algorithms are being implemented across a breadth of industries to enable technological innovations. In this paper, we study the problem of computer vision based customer tracking in retail industry. To this end, we introduce a dataset collected from a camera in an office environment where participants mimic various behaviors of customers in a supermarket. In addition, we describe an illustrative example of the use of this dataset for tracking participants based on a head tracking model in an effort to minimize errors due to occlusion. Furthermore, we propose a model for recognizing customers and staff based on their movement patterns. The model is evaluated using a real-world dataset collected in a supermarket over a 24-hour period that achieves 98% accuracy during training and 93% accuracy during evaluation.
@article{arxiv.2005.08009,
title = {Towards in-store multi-person tracking using head detection and track heatmaps},
author = {Aibek Musaev and Jiangping Wang and Liang Zhu and Cheng Li and Yi Chen and Jialin Liu and Wanqi Zhang and Juan Mei and De Wang},
journal= {arXiv preprint arXiv:2005.08009},
year = {2020}
}