Abstract
In the present-day scenario of the retail environment, there is a tendency of customers to do prolonged shopping. During their constant efforts to purchase products of their choice, they bat around the entire store. They choose a product and continue exploring for more. While their further exploration, there is a possibility that they might encounter a better product that may satisfy their needs. So, there is a tendency that they may pick up the new product, compare with the existing product and leave the latter behind if they find the new product to be of a better purpose to their use, causing the initial product to be misplaced. There might be a possibility that empty spaces be created between products that might look sparse and lower stock display if not properly monitored. The primary goal of this research is smart space management and personalized store assortment by using computer vision. That is, wherever there are empty spaces created, we constantly monitor, and whenever there is any product misplaced, we send an automated notification to corresponding staff. We use state-of-the-art computer vision technology to address this issue. All the processing is done in real time and the system is found to be functionally very stable and works under all ideal conditions.
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Annexure—A—Abbreviations
Annexure—A—Abbreviations
CNN | Convolutional Neural Network |
FRCNN | Faster Regional CNN |
mAP | Mean Average Precision |
IoU | Intersection over Union |
RPN | Region Proposal Network |
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Dantu, N.V., Vasudevan, S.K. (2021). Real-Time Retail Smart Space Optimization and Personalized Store Assortment with Two-Stage Object Detection Using Faster Regional Convolutional Neural Network. In: Thampi, S.M., Gelenbe, E., Atiquzzaman, M., Chaudhary, V., Li, KC. (eds) Advances in Computing and Network Communications. Lecture Notes in Electrical Engineering, vol 736. Springer, Singapore. https://doi.org/10.1007/978-981-33-6987-0_33
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DOI: https://doi.org/10.1007/978-981-33-6987-0_33
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