Towards in-store multi-person tracking using head detection and track heatmaps
This addresses tracking and identification in retail settings, but it is incremental as it builds on existing computer vision methods with new datasets and minor model adaptations.
The paper tackled customer tracking in retail by introducing a dataset from an office environment mimicking supermarket behaviors and proposing a model for recognizing customers and staff based on movement patterns, achieving 93% accuracy in evaluation on a real-world supermarket dataset.
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.