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

TL;DR

This paper presents a new approach for multi-person customer tracking in retail environments using head detection and heatmaps, supported by a novel dataset and a movement pattern recognition model.

Contribution

It introduces a new dataset for customer tracking, a head detection-based tracking method, and a movement pattern recognition model for distinguishing customers from staff.

Findings

Achieved 98% accuracy during training

Achieved 93% accuracy during evaluation

Demonstrated effectiveness in real-world supermarket data

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.