Abstract
Smart parking systems are becoming increasingly popular in smart cities due to their numerous benefits. Unlike traditional systems that require drivers to spend a lot of time searching for parking spots, smart parking systems use a combination of edge computing platforms, cloud services, and user applications based on videos and sensor data. This paper presents our system design and implementation of smart parking. Our proposed architecture uses edge computing to process most workloads in video processing, which overcomes network bandwidth obstacles. We deployed our prototype at our institution campus using Jetson Xavier boards for testing. Our experimental results show that we achieve video processing performance at the edge side by up to 30 FPS. We developed two AI models that can recognize vehicle license plates and manage parking slots. We used certified datasets for training and testing, and the models offer an accuracy of up to 99.6%.
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We acknowledge Ho Chi Minh City University of Technology (HCMUT), VNU-HCM for supporting this study.
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Pham-Quoc, C., Bang, T. (2023). Towards a Smart Parking System with the Jetson Xavier Edge Computing Platform. In: Dao, NN., Thinh, T.N., Nguyen, N.T. (eds) Intelligence of Things: Technologies and Applications. ICIT 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 187. Springer, Cham. https://doi.org/10.1007/978-3-031-46573-4_36
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DOI: https://doi.org/10.1007/978-3-031-46573-4_36
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