5^th World Machine Learning and Deep Learning Congress

Biography

Biography: Rohit Agarwal & Gaurav Pawar

Abstract

We have used image recognition and machine learning technology to automate some of the time consuming and error prone auditing use cases pertinent for SME retail stores (aka mom-n-pop stores) like which all SKU type are present in particular store. Check if the store has put out advertising of the brand as was agreed upon. Current process of store auditing is conducted through feet on street sales force/external auditing agencies which is manual, biased, time consuming, costly affair and is not scalable. Leveraging the state of art deep learning image recognition technology, our platform helps in automating store auditing process by acting as eyes for tracking all types of in-store visibility executions like window displays, POS material and outdoor advertising/banners with high degree of precision (>90%) which is much better than classical approached like SVM (~80%). The platform can analyze millions of retail store images to generate actionable insights for brands/company. Role of the sales force is limited to take pictures of the stores and upload them to our platform. Most of the current image analytics platform works with high quality images of organized environment like supermarkets, this makes our platform different as it has been specifically designed for mom-n-pop store setup which symbolizes unorganized shelves and cluttered environment. The images obtained are also low quality as they are typically shot by sales force using low quality mobile camera. Some of the common issues with these images are low light, partial visibility, occlusion, glare, incorrect angle, etc. In this, we intend to give a technical overview of the platform, highlight its capability to analyze images of varying nature, showcase few use cases in SME domain that can be implemented using this platform.