Using image analytics to monitor retail store shelves


Using image analytics to monitor the contents and status of retail store shelves is an rising trend with increasing business importance. Detecting and identifying multiple objects on store shelves involves a range of technical challenges. The explicit nature of product package style, the arrangement of products on shelves, and the necessity to work in unconstrained environments are simply a few of the issues that has to be addressed. We have a tendency to justify how we addressed these challenges in an exceedingly system for monitoring planogram compliance, developed as half of a project with Tesco, a giant multinational retailer. The new system offers store personnel an instant view of shelf status and a listing of action things for restocking shelves. The core of the system relies on its ability to attain high rates of product recognition, despite the terribly tiny visual differences between some product. This paper covers how state-of-the-art ways for object detection behave when applied to the current problem. We tend to conjointly describe the innovative aspects of our implementation for size-scale-invariant product recognition and fine-grained classification.

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