Visible to the public Smart Surveillance as an Edge Service for Real-Time Human Detection and Tracking

TitleSmart Surveillance as an Edge Service for Real-Time Human Detection and Tracking
Publication TypeConference Paper
Year of Publication2018
AuthorsNikouei, S. Y., Chen, Y., Faughnan, T. R.
Conference Name2018 IEEE/ACM Symposium on Edge Computing (SEC)
Date PublishedOct. 2018
ISBN Number978-1-5386-9445-9
Keywordscomposability, Computer architecture, critical data-driven tasks, decision making, dynamic data-driven tasks, edge detection, edge service, edge-fog layers, feature extraction, fog, Image edge detection, Metrics, object detection, object tracking, performance evaluation, policy makers, pubcrawl, Real time Human Detection and Tracking, real-time human detection, resilience, Resiliency, Scalability, security, situational awareness, smart surveillance, surveillance, Task Analysis, tracking tasks, urban planners, urban surveillance, video surveillance

Monitoring for security and well-being in highly populated areas is a critical issue for city administrators, policy makers and urban planners. As an essential part of many dynamic and critical data-driven tasks, situational awareness (SAW) provides decision-makers a deeper insight of the meaning of urban surveillance. Thus, surveillance measures are increasingly needed. However, traditional surveillance platforms are not scalable when more cameras are added to the network. In this work, a smart surveillance as an edge service has been proposed. To accomplish the object detection, identification, and tracking tasks at the edge-fog layers, two novel lightweight algorithms are proposed for detection and tracking respectively. A prototype has been built to validate the feasibility of the idea, and the test results are very encouraging.

Citation Keynikouei_smart_2018