Applying Image Processing for Detecting On-Shelf Availability and Product Positioning in Retail Stores

Lack of availability of goods and/or the improper positioning of products on the shelves of a retail store can result in loss of sales to a retailer. Visual audits are undertaken by the retailer's staff and the staff of the FMCG product companies, (whose products are stocked in the retail shelves), to discover out-of-stock and misplaced products in a retailer's shelf. In this paper, a method of automating the process of manual inspection has been described. The paper also demonstrates that by applying image processing techniques (available in MATLAB 2013a), it is possible to identify and count the front-facing products, as well as detect void spaces on the shelf. Images from a video stream (such as from a security camera) can also be analyzed to count the number of facings of a specific product on a shelf and identify if they are placed face-up, as should be the case. The image processing approach proposed in the paper will primarily enable proper positioning of products on the shelf the front row. While that may seem as a limitation for inventory counting, it is actually an important parameter for product manufacturers who usually rent shelf space and positions at a premium and mandate the retailers to place specific products at specific shelves. The incremental change that the paper proposes is to extend the use of feature extraction in image processing to highlight incorrect placement and positioning of items on the shelves. The implemented solution does not require significant additional infrastructure costs.