A STUDY ON APPLICATION OF MACHINE LEARNING AND COMPUTER VISION FOR RETAIL PROJECTS
The motivation for developing computer vision is the human vision system which is the richest sense that we have. To us, vision seems an easy task of just seeing objects in daily life and identifying them, but in reality, our eyes along with the brain are processing information of around 50 images every second with millions of pixels in each image. Most of these images obtained are currently just looked at by people. The challenging task is to process images from all these cameras and allow automation of tasks never before considered. Neural networks help us in making cameras intelligent enough to understand the images it captures. Convolutional neural networks (CNN) are trained to give image classification results of good accuracy, with the challenge to improve utilization of computing resources. Google Net is in its essence a deep CNN that uses inception architecture to attain leading edge results for classification and detection problems. In this paper, a study was made on applications of computer vision techniques in retail and customer strategic projects. Further, it was analyzed that if cameras trained with CNN can work well enough to be deployed in retail market scenarios to automate sales and stock supervision.