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UAV based cost-effective real-time abnormal event detection using edge computing

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Abstract

Recent advancements in computer vision led to the development of a real-time surveillance system which ensures the safety and security of the people in public places. An aerial surveillance system will be advantageous in this scenario using a platform like Unmanned Aerial Vehicle (UAV) will be very reliable and can be considered as a cost-effective option for this task. To make the system fully autonomous, we require real-time abnormal event detection. But, this is computationally complex and time-consuming due to the heavy load on the UAV, which affords limited processing and payload capacity. In this paper, we propose a cost-effective approach for aerial surveillance in which we move the large computation tasks to the cloud while keeping limited computation on-board UAV device using edge computing technique. Further, our proposed system will maintain the minimum communication between UAV and cloud. Thus it not only reduces the network traffic but also reduces the end-to-end delay. The proposed method is based on the state-of-the-art YOLO (You Only Look Once) technique for real-time object detection deployed on edge computing device using Intel neural compute stick Movidius VPU (Vision Processing Unit), and we applied abnormal event detection using motion influence map on the cloud. Experimental results demonstrate that the proposed system reduces the end-to-end delay. Further, Tiny YOLO is six times faster while processing the frames per second (fps) when compared to other state-of-the-art methods.

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Authors and Affiliations

  1. National Institute of Technology Karnataka, Surathkal, India

    Md Shahzad Alam,  Natesha B. V.,  Ashwin T. S. & Ram Mohana Reddy Guddeti

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  1. Md Shahzad Alam
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  2. Natesha B. V.
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  3. Ashwin T. S.
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  4. Ram Mohana Reddy Guddeti
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Correspondence to Ashwin T. S..

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Alam, M.S., Natesha B. V., Ashwin T. S. et al. UAV based cost-effective real-time abnormal event detection using edge computing. Multimed Tools Appl 78, 35119–35134 (2019). https://doi.org/10.1007/s11042-019-08067-1

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