Abstract
With the rapid growth in the use of IoT devices in monitoring and surveillance environment, the amount of data generated by these devices is increased exponentially. There is a need for efficient computing architecture to push the intelligence and data processing close to the data source nodes. Fog computing will help us to process and analyze the video at the edge of the network and thus reduces the service latency and network congestion. In this paper, we develop fog computing infrastructure which uses the deep learning models to process the video feed generated by the surveillance cameras. The preliminary experimental results show that using different deep learning models (DNN and SNN) at the different levels of fog infrastructure helps to process the video and classify the vehicle in real time and thus service the delay-sensitive applications.
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Acknowledgements
This work has been supported by the Visvesvaraya Ph.D. Scheme for Electronics and IT (Media Lab Asia), the Department of MeitY, Government of India. This work has been carried out at the Department of Information Technology, NITK Surathkal, Mangalore, India.
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Natesha, B.V., Guddeti, R.M.R. (2021). Fog-Based Video Surveillance System for Smart City Applications. In: Bhateja, V., Peng, SL., Satapathy, S.C., Zhang, YD. (eds) Evolution in Computational Intelligence. Advances in Intelligent Systems and Computing, vol 1176. Springer, Singapore. https://doi.org/10.1007/978-981-15-5788-0_70
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DOI: https://doi.org/10.1007/978-981-15-5788-0_70
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