Abstract
Manual traffic surveillance can be a daunting task as Traffic Management Centers operate a myriad of cameras installed over a network. Injecting some level of automation could help lighten the workload of human operators performing manual surveillance and facilitate making proactive decisions which would reduce the impact of incidents and recurring congestion on roadways. This article presents a novel approach to automatically monitor real time traffic footage using deep convolutional neural networks and a stand-alone graphical user interface. The authors describe the results of research received in the process of developing models that serve as an integrated framework for an artificial intelligence enabled traffic monitoring system. The proposed system deploys several state-of-the-art deep learning algorithms to automate different traffic monitoring needs. Taking advantage of a large database of annotated video surveillance data, deep learning-based models are trained to detect queues, track stationary vehicles, and tabulate vehicle counts. A pixel-level segmentation approach is applied to detect traffic queues and predict severity. Real-time object detection algorithms coupled with different tracking systems are deployed to automatically detect stranded vehicles as well as perform vehicular counts. At each stage of development, interesting experimental results are presented to demonstrate the effectiveness of the proposed system. Overall, the results demonstrate that the proposed framework performs satisfactorily under varied conditions without being immensely impacted by environmental hazards such as blurry camera views, low illumination, rain, or snow.
Highlights
Monitoring traffic effectively has long been one of the most important efforts in transportation engineering
Most traffic monitoring centers rely on human operators to track the nature of traffic flows and oversee any incident happening on the roads
It is worthwhile to note that most present-day traffic monitoring activity happens at the Traffic Management Centers (TMCs) through vision-based camera systems
Summary
Monitoring traffic effectively has long been one of the most important efforts in transportation engineering. Most traffic monitoring centers rely on human operators to track the nature of traffic flows and oversee any incident happening on the roads. As humans are prone to inaccuracies and subject to fatigue, the results often involve certain discrepancies. It is, in best interests to develop automated traffic monitoring tools to diminishing the workload of human operators and increase the efficiency of output. It is not surprising that automatic traffic monitoring systems have been one of the most important research endeavors in intelligent transportation systems. It is worthwhile to note that most present-day traffic monitoring activity happens at the Traffic Management Centers (TMCs) through vision-based camera systems. FigureFig1u. reP1r.oPproospeodsedfrfornont-te-enndd GGUUII--bbaasseeddsyssytesmtemwitwh aitlghoariltghomrsitahnmd straafnfidc dtartaaffibacsedpartoacbeassseedpinrothceessed in thebbaackckenedn.dT.oTvoisvuiasluizaelitzheetdheemdoenmstorantsiotrnaotifotnheopfrtohpeopserdopGoUsIebdaGseUdIpblaatsfoerdmp, lraetffeorrtmo [,4r]e. fer to [4]
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