Video-Based Vehicle Detection and Classification System for Real-Time Traffic Data Collection Using Uncalibrated Video Cameras

TLDR: A video-based vehicle detection and classification (VVDC) system was developed for truck data collection using wide-ranging available surveillance cameras and the accuracy for vehicle detection was above 97%, and the total truck count error was lower than 9% for all three tests, indicating that the video image processing method developed is indeed a viable alternative for Truck data collection.

Abstract: Length-based vehicle classification data are important inputs for traffic operation, pavement design, and transportation planning. However, such data are not directly measurable by single-loop detectors, the most widely deployed type of traffic sensor in the existing roadway infrastructure. In this study a video-based vehicle detection and classification (VVDC) system was developed for truck data collection using wide-ranging available surveillance cameras. Several computer vision-based algorithms were developed or applied to extract background image from a video sequence, detect presence of vehicles, identify and remove shadows, and calculate pixel-based vehicle lengths for classification. Care was taken to handle robustly negative effects resulting from vehicle occlusions in the horizontal direction and slight camera vibrations. The pixel-represented lengths were exploited to distinguish long vehicles from short vehicles; hence the need for complicated camera calibration can be eliminated. These algorithms were implemented in the prototype VVDC system using Microsoft Visual C#. As a plug-and-play system, the VVDC system is capable of processing both digitized image streams and live video signals in real time. The system was tested at three test locations under different traffic and environmental conditions. The accuracy for vehicle detection was above 97%, and the total truck count error was lower than 9% for all three tests. This indicates that the video image processing method developed for vehicle detection and classification in this study is indeed a viable alternative for truck data collection.