Length-based vehicle classification data are important inputs for traffic operation, pavement design, and transportation planning. However, such data are not directly measurable by singleloop 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 robustly handle negative impacts resulting from vehicle occlusions in the horizontal direction and slight camera vibrations. The pixel-represented lengths were exploited to relatively distinguish long vehicles from short vehicles, and 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 & 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 percent and the total truck count error was lower than 9 percent 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.

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Traffic surveillance cameras are becoming a viable replacement for inductive loop detectors. The effectiveness of these cameras, however, depends on video image processing algorithms that can alleviate common problems such as shadows, vehicle occlusion, reflection, and camera shake. Shadows have proved to be a major source of error in the detection and classification of vehicles. Three algorithms of increasing complexity are proposed to address the shadow problem. The algorithms each address the need to remove cast shadows from vehicles while preserving self-shadows, or those areas of a vehicle that are hidden from illumination. They are also geared toward real-time analysis, which requires that they can be implemented efficiently and cannot have complex training or learning requirements. The dual-pass Otsu method of shadow removal was the simplest in application but had the poorest performance. The proposed region growing technique, though showing considerable promise, failed when the pixel intensity varied widely in the shadow region. The last technique used edge imaging to recognize shadows as areas with few edges or with edges substantially similar to the background. This method clearly outperformed the other methods and was subsequently proved in a separate paper describing a prototype vehicle detection and classification system.