Читать книгу Handbook of Intelligent Computing and Optimization for Sustainable Development - Группа авторов - Страница 90

In this section, we highlight the major findings of this study. We utilize the GMG background subtraction algorithm for extracting the foreground information capturing non-stationary garments from the video of a surveillance camera. Using this foreground information, we are able to detect the garment regions which are then clustered to obtain the entire garment. We leverage the Mask R-CNN framework to detect customers from its object detection performed for the “person” class. Subsequently, we obtain the wrist feature points of these customers using OpenPose human pose estimation framework. Using these pieces of information, we propose a quantitative metric, known as the confidence score, which indicates the degree to which a customer is interested in a given garment. We take the Euclidean distance between the centroid of a garment and the coordinates of the wrist feature points of a customer and use the area of the garment to calculate the confidence score for the given pair of customer and garment. We analyze the variation of the average confidence score of garments of interest with a confidence threshold to generate meaningful evaluations.