Towards in-store multi-person tracking using head detection and track heatmaps

16 May 2020  ·  Aibek Musaev, Jiangping Wang, Liang Zhu, Cheng Li, Yi Chen, Jialin Liu, Wanqi Zhang, Juan Mei, De Wang ·

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.

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