| Summary: | 碩士 === 國立交通大學 === 資訊科學與工程研究所 === 107 === In this study, we designed and implemented a smart shopping cart prototype system to verify the related technology. The shopping cart has a camera and communication interface for recording and transmitting images of the shopping cart, and uses a deep learning network to analyze the content of each frame. To detect the shopping behavior of instill and pull it off, perform the mechanism of action analysis on the time axis, and present the list of the purchased products to the user on the virtual shopping Cart-APP. In performance, the classification accuracy based on Faster R-CNN, YOLOv2 and YOLOv2-Tiny frames falls between 93.0% and 90.3%, and the speeds are up to 5 fps, 39 fps and 50 fps respectively. Shopping action recognition used to distinguish among “No hand”, “Empty hand” and “Holding item” time, the correct rate is 96%, and the time error is 0.119s. The shopping event detect the change of items in the shopping cart that made by the consumer. The shopping events are divide into four classes such as: “No Change”, “Insert”, “Take away” and “Change in Items”. The accuracy of shopping events are 97.9%. In summary, Proposed a smart shopping cart solution based on image action recognition, combine the action recognition and deep learning on data to track the list of products in the shopping cart, for the visual aspects implement the prototype system.
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