Surveillance of abnormal behavior in elevators based on edge computing

Yan Qi; Ping Lou; Junwei Yan; Jiwei Hu

doi:10.1117/12.2541397

27 November 2019 Surveillance of abnormal behavior in elevators based on edge computing

Yan Qi, Ping Lou, Junwei Yan, Jiwei Hu

Proceedings Volume 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence; 1132114 (2019) https://doi.org/10.1117/12.2541397
Event: The Second International Conference on Image, Video Processing and Artifical Intelligence, 2019, Shanghai, China

Abstract

Edge computing is an extension of the cloud computing paradigm that shifts part of computing data, applications and services from the cloud server to the network edge, providing low-latency, mobility and location-aware support for delaysensitive applications. The elevators in the high-rise buildings are geographically distributed and movable. Safety and reliability of elevators have attracted people’s attention. Security problem in the elevator is a key issue, especially in emergencies requiring fast response and low latency. In this paper, an elevator abnormal behavior video surveillance system is designed and developed using edge computing paradigm. The recognition of abnormal image sequences and the evaluation of abnormal behavior are realized. Collecting, processing, and analyzing video images are completed at the network edge in real time. The Edge computing nodes are distributed and deployed according to the geographic location of the elevator. The edge nodes are based on mobile embedded devices, and use the computing resources of the embedded devices to implement edge computing at the network edge. Through the edge network, there are several edge nodes based clusters being built to perform distributed computation tasks.

Citation Download Citation

Yan Qi, Ping Lou, Junwei Yan, and Jiwei Hu "Surveillance of abnormal behavior in elevators based on edge computing", Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 1132114 (27 November 2019); https://doi.org/10.1117/12.2541397

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KEYWORDS

Video

Clouds

Video surveillance

Target detection

Video processing

Detection and tracking algorithms

Computing systems

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