Abstract
Recently, deep neural network and cloud computing based intelligent video surveillance technology are growing interests in the industrial and academia. The synergy with both technologies emerges as a key role of the public safety and video surveillance in the field. Reflecting these trends, we have been studying a cloud-based intelligent video analytic service using deep learning technology. INCUVAS (cloud-based INCUbating platform for Video Analytic Service) is a platform that continuously enhances the video analysis performance by updating real-time dataset with the deep neural network on a cloud environment. The goal of this cloud service can provide continuous performance enhancement and management using image dataset from the real environment. In this paper, we consider the design requirements for online deep learning based intelligent video analytics service.
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Acknowledgments
This work was supported by the Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIP). (2017-0-00207, Development of Cloud-based Intelligent Video Security Incubator Platform).
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Lim, KS., Lee, SH., Han, J.W., Kim, GW. (2019). Design Considerations for an Intelligent Video Surveillance System Using Cloud Computing. In: Park, J., Shen, H., Sung, Y., Tian, H. (eds) Parallel and Distributed Computing, Applications and Technologies. PDCAT 2018. Communications in Computer and Information Science, vol 931. Springer, Singapore. https://doi.org/10.1007/978-981-13-5907-1_9
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DOI: https://doi.org/10.1007/978-981-13-5907-1_9
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