Monitoring Traffic Behaviors: Lane Detection and Speed Calculation

  • Saim Rasheed Department of Information Technology, Faculty of Computing and IT King Abdulaziz University


In this work, we propose an image processing-based hybrid technique that provides assistance in detecting certain patterns in traffic infractions committed by drivers on the roads. The proposed technique is based on the speed estimation using video data in conjunction with tracking methods. Our hybrid proposal comprises two parts. First, we propose a method to detect the road lanes using Hough transform. Second, we detect the vehicles in the video datasets using Haar Cascade methodology and then track those vehicles for their speed estimation and for monitoring the driving patterns. In addition, the types of infractions that a driver can commit while driving are also detailed. The most significant cases in which the infractions are determined is when the driver makes a rapid and continuous change of lanes, parking at inappropriate places and prohibited U-turns. The results are provided in terms of vehicle detection and speed estimation. Our results and analysis reveal that the proposed method can assist in monitoring the driving patterns and detecting traffic infractions.

How to Cite
Rasheed, S. (2020). Monitoring Traffic Behaviors: Lane Detection and Speed Calculation. Transactions on Machine Learning and Artificial Intelligence, 8(5), 52-64.