Traffic Camera Calibration via Vehicle Vanishing Point Detection

TL;DR

This paper introduces a CNN-based method for calibrating traffic surveillance cameras by detecting vehicle vanishing points, estimating camera parameters, and demonstrating competitive results with fewer requirements.

Contribution

A novel CNN approach utilizing diamond space for vanishing point detection from vehicle images, improving calibration accuracy and efficiency.

Findings

Achieves competitive calibration accuracy on BrnoCarPark dataset.

Requires fewer assumptions and inputs than existing methods.

Effectively estimates camera focal length and road plane orientation.

Abstract

In this paper we propose a traffic surveillance camera calibration method based on detection of pairs of vanishing points associated with vehicles in the traffic surveillance footage. To detect the vanishing points we propose a CNN which outputs heatmaps in which the positions of vanishing points are represented using the diamond space parametrization which enables us to detect vanishing points from the whole infinite projective space. From the detected pairs of vanishing points for multiple vehicles in a scene we establish the scene geometry by estimating the focal length of the camera and the orientation of the road plane. We show that our method achieves competitive results on the BrnoCarPark dataset while having fewer requirements than the current state of the art approach.