Deep learning-based models are trained to detect queues, track stationary vehicles, and tabulate vehicle counts. Real-time object detection algorithms coupled with different tracking systems are deployed to automatically detect stranded vehicles.
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 stages 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