Vehicle-counting with Automatic Region-of-Interest and Driving-Trajectory detection

TL;DR

This paper presents an automated vehicle counting method that identifies the region of interest and vehicle trajectories in traffic videos, enabling effective analysis with Pan-Tilt-Zoom cameras often used in developing countries.

Contribution

The work introduces a novel automated approach for ROI and trajectory detection in traffic videos, reducing the need for human input and enhancing applicability in diverse camera setups.

Findings

Achieved 57.05% intersection over union for ROI detection

Attained 17.44% mean absolute error in vehicle counting

Demonstrated feasibility with Pan-Tilt-Zoom cameras in real-world scenarios

Abstract

Vehicle counting systems can help with vehicle analysis and traffic incident detection. Unfortunately, most existing methods require some level of human input to identify the Region of interest (ROI), movements of interest, or to establish a reference point or line to count vehicles from traffic cameras. This work introduces a method to count vehicles from traffic videos that automatically identifies the ROI for the camera, as well as the driving trajectories of the vehicles. This makes the method feasible to use with Pan-Tilt-Zoom cameras, which are frequently used in developing countries. Preliminary results indicate that the proposed method achieves an average intersection over the union of 57.05% for the ROI and a mean absolute error of just 17.44% at counting vehicles of the traffic video cameras tested.