Headway and Following Distance Estimation using a Monocular Camera and Deep Learning

Zakaria Charouh, Amal Ezzouhri, Mounir Ghogho, Zouhair Guennoun

Abstract: We propose a system for monitoring the headway and following distance using a roadside camera and deep learning-based computer vision techniques. The system is composed of a vehicle detector and tracker, a speed estimator and a headway estimator. Both motion-based and appearance-based methods for vehicle detection are investigated. Appearance-based methods using convolutional neural networks are found to be most appropriate given the high detection accuracy requirements of the system. Headway estimation is then carried out using the detected vehicles on a video sequence. The following distance estimation is carried out using the headway and speed estimations. We also propose methods to assess the performance of the headway and speed estimation processes. The proposed monitoring system has been applied to data that we have collected using a roadside camera. The root mean square error of the headway estimation is found to be around 0.045 seconds.