End-to-End Traffic Monitoring and Management in Real-Time Road Traffic

Abstract

The detection of vehicles and pedestrians on the road is one of the most challenging problems in object detection and autonomous vehicles. This paper reports a novel intelligent traffic monitoring and management system using You Only Look Once (YOLO) and OpenCV tracker. A new database named 'Kannur University Vehicle Database (KNUVDB)’ is created and used for the purpose of studying vehicle detection., in addition to the available datasets in literature. We focus on counting the vehicles after they have been detected and tracked. Later., performs the traffic update by using the vehicle count. The proposed method provides better detection accuracy on the real-time traffic video dataset available in the literatures and also on the KNUVDB dataset. Experimental studies have shown that Discriminative Correlation Filter with Channel and Spatial Reliability (CSRT) and Kernelized Correlation Filter (KCF) provide better performance than other OpenCV trackers. In KNUVDB dataset., YOLO-CSRT gives 100% accuracy and YOLO-KCF provides 90.90% accuracy in vehicle detection., tracking and counting. In real-time road traffic video dataset., YOLO-CSRT provides 100% accuracy and YOLO-KCF provides 93.70 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> accuracy in vehicle detection., tracking., and counting accuracy.