A Logo-Based Approach for Recognising Multiple Products on a Shelf

This paper addresses detecting, localizing and recognizing various grocery products in retail store images. Our object recognition algorithm achieves this goal using just one image per product for training, assuming that the category of the products (like cereals, rice, etc.) is known. This algorithm uses logo detection as a precursor to product recognition. So, the first step involves detecting and classifying products, at a broader level, based on their brands. The second step is the finer classification step for recognizing and localizing the exact product label, which involves using colour information. This hierarchical approach limits the confusion in classifying similar looking products and outperforms product recognition that was implemented without logo detection. The algorithm was tested on 80 annotated grocery shelf images containing 238 different products that fall under 3 categories. This facilitates smarter inventory management in retail stores on a large scale and on a day to day basis for the visually impaired people.