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Performance of video processing at the edge for crowd-monitoring applications


Camille Ballas, Mark Marsden, Dian Zhang, Noel O'Connor, Suzanne Little

Publication Type: 
Refereed Conference Meeting Proceeding
Video analytics has a key role to play in smart cities and connected community applications such as crowd counting, activity detection, event classification, traffic counting etc. Using a cloud-centric approach where data is funnelled to a central processor presents a number of key problems such as available bandwidth, real-time responsiveness and personal data privacy issues. With the development of edge computing, a new paradigm for smart data management is emerging. Raw video feeds can be pre-processed at the point of capture while integration and deeper analytics is performed in the cloud. In this paper we explore the capacity of video processing at the edge and shown that basic image processing can be achieved in near real-time on lowpowered gateway devices. We have also investigated deep learning model capabilities for crowd counting in this context showing that its performance is highly dependent on the input size and rescaling video frames can optimise processing and performance. Increased edge processing resolves a number of issues in video analytics for crowd monitoring applications.
Conference Name: 
2018 IEEE 4th World Forum on Internet of Things
Digital Object Identifer (DOI): 
Publication Date: 
Research Group: 
Dublin City University (DCU)
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