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

• URL: http://arxiv.org/abs/2005.08009v2
• Date: Thu, 2 Jul 2020 03:22:46 GMT
• Title: Towards in-store multi-person tracking using head detection and track heatmaps
• Authors: Aibek Musaev, Jiangping Wang, Liang Zhu, Cheng Li, Yi Chen, Jialin Liu, Wanqi Zhang, Juan Mei, De Wang
• Abstract summary: We introduce a dataset collected from a camera in an office environment where participants mimic various behaviors of customers in a supermarket. 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.
• Score: 11.318061963422807
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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