Accurate Automatic Camera Calibration on Low-Quality CCTV Traffic Video Streams

Abstract

This paper focuses on extracting vehicle trajectory from low-quality, uncalibrated traffic cameras via fully automatic camera calibration and homography transformations. We analyze video streams from 511 traffic surveillance cameras in varying areas and arbitrary relative locations and orientations to roads. We propose a novel automatic calibration methodology that is used to transform the video streams to a top-down perspective from a minute of the video stream. Using this perspective, we can extract both lane changes and vehicle speed. Our automatic camera calibration algorithm is inspired by two vanishing point based automatic camera calibration methods. We implement more accurate and robust methods for the detection of both vanishing points. We show that YOLOv4 combined with DeepSORT is 12.5 times faster than the leading vehicle detection model and achieves a 7.81% higher average precision and 6.04% higher mean average precision in low quality traffic surveillance cameras. We use this model for a more robust estimation of the first vanishing point and more robust object tracking. Furthermore, we propose a fast, novel “guess and check” algorithm for the detection of the second vanishing point to ensure accurate second vanishing point detection. We show that our camera calibration algorithm produces fewer inaccuracies than the state-of-the-art automatic camera calibration methodology in [1] through qualitative results.