Abstract
The smart transportation system is one of the most essential parts in a smart city roadmap. The smart transportation applications are equipped with CCTV to recognize a region of interest through automated object detection methods. Usually, such methods require high-complexity image classification techniques and advanced hardware specification. Therefore, the design of low-complexity automated object detection algorithms becomes an important topic in this area. A novel technique is proposed to detect a moving object from the surveillance videos based on CPU (central processing units). We use this method to determine the area of the moving object(s). Furthermore, the area will be processed through a deep convolutional nets-based image classification in GPU (graphics processing units) in order to ensure high efficiency and accuracy. It cannot only help to detect object rapidly and accurately, but also can reduce big data volume needed to be stored in smart transportation systems.
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Muchtar, K. et al. (2019). An Efficient Event Detection Through Background Subtraction and Deep Convolutional Nets. In: Chang, CY., Lin, CC., Lin, HH. (eds) New Trends in Computer Technologies and Applications. ICS 2018. Communications in Computer and Information Science, vol 1013. Springer, Singapore. https://doi.org/10.1007/978-981-13-9190-3_16
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DOI: https://doi.org/10.1007/978-981-13-9190-3_16
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