Artificial Intelligence Enabled Traffic Monitoring System

TL;DR

This paper introduces an AI-powered traffic monitoring system utilizing deep learning algorithms for real-time detection, tracking, and analysis of traffic conditions, aiming to assist traffic management centers in proactive decision-making.

Contribution

The paper presents a comprehensive AI framework integrating multiple deep learning models for automated traffic surveillance and analysis, improving accuracy and robustness over traditional manual methods.

Findings

Effective detection of traffic queues and vehicle counts

Robust performance under adverse environmental conditions

Accurate identification of stranded vehicles and traffic severity

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 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 snow.