Traffic monitoring using video analytics in clouds

Conference item


Abdullah, Tariq, Anjum, Ashiq, Tariq, M. Fahim, Baltaci, Yusuf and Antonopoulos, Nikolaos 2014. Traffic monitoring using video analytics in clouds. IEEE. https://doi.org/10.1109/UCC.2014.12
AuthorsAbdullah, Tariq, Anjum, Ashiq, Tariq, M. Fahim, Baltaci, Yusuf and Antonopoulos, Nikolaos
Abstract

Traffic monitoring is a challenging task on crowded roads. Traditional traffic monitoring procedures are manual, expensive, time consuming and involve human operators. They are subjective due to the very involvement of human factor and sometimes provide inaccurate/incomplete monitoring results. Large scale storage and analysis of video streams were not possible due to limited availability of storage and compute resources in the past. Recent advances in data storage, processing and communications have made it possible to store and process huge volumes of video data and develop applications that are neither subjective nor limited in feature sets. It is now possible to implement object detection and tracking, behavioural analysis of traffic patterns, number plate recognition and automate security and surveillance on video streams produced by traffic monitoring and surveillance cameras. In this paper, we present a video stream acquisition, processing and analytics framework in the clouds to address some of the traffic monitoring challenges mentioned above. This framework provides an end-to-end solution for video stream capture, storage and analysis using a cloud based GPU cluster. The framework empowers traffic control room operators by automating the process of vehicle identification and finding events of interest from the recorded video streams. An operator only specifies the analysis criteria and the duration of video streams to analyse. The video streams are then automatically fetched from the cloud storage, decoded and analysed on a Hadoop based GPU cluster without operator intervention in our framework. It reduces the latencies in video analysis process by porting its compute intensive parts to the GPU cluster. The framework is evaluated with one month of recorded video streams data on a cloud based GPU cluster. The results show a speedup of 14 times on a GPU and 4 times on a CPU when compared with one human operator analysing the same amount of video streams data.

KeywordsCloud computing; Video stream analysis; Machine learning; GPUs
Year2014
JournalProceedings of the 7th International Conference on Utility and Cloud Computing (UCC)
PublisherIEEE
Digital Object Identifier (DOI)https://doi.org/10.1109/UCC.2014.12
Web address (URL)http://hdl.handle.net/10545/620941
hdl:10545/620941
ISBN9781479978816
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Publication dates12 Dec 2014
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Deposited21 Nov 2016, 16:16
ContributorsUniversity of Derby
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