Deep Learning based Image Processing for Cashier-less Self-Checkout Methodology

In recent years, shopping experiences are becoming more advanced. These include the attempts of market shelves as well as the currently booming online shopping. Online shopping has a better convenience but not yet accepted on a large scale by many people. Retail shops still retain greater response by the users and thus the retailers are moving towards an attempt of cashier-less shopping. A major problem of retail shops is that the people have crunch-time for shopping and cannot afford the waiting time at the checkout counters. Addressing this problem, we have developed a shopping style which saves time of checkout and also the time of maintaining social distancing queues. This research paper presents a stereo vision-based AI system which is useful to monitor the customers while shopping and also the items which are added or replaced in the virtual cart. The customers can directly walk out of the store after shopping and the final order cost of the shopping will be evaluated. This amount will be charged to the customer’s account. The system makes sure that there are no errors made during the evaluation and there are no charges for products which are not brought home. To achieve all this, the system uses image processing, object detection and face recognition algorithms that are widely practiced at present. The system also uses sensors like RFID tags and pressure sensors for weight measurement and detection of products on the shelves.