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A Video-based Vehicle Detection and Classification System - ramki.2011 - 10-04-2017 A Video-based Vehicle Detection and Classification System for Real-time Traffic Data Collection Using Uncalibrated Video Cameras [attachment=17194] INTRODUCTION Due to the considerable differences in performance, size, and weight between long vehicles (LVs) and short vehicles (SVs), length-based vehicle classification data are of fundamental importance for traffic operation, pavement design, and transportation planning. Highway Capacity Manual (1) requires adjustments to heavy-vehicle volumes in capacity analysis. The geometric design of a roadway, such as horizontal alignment and curb heights, is affected by the different moving characteristics of LVs due to their heavy weight, inferior braking, and large turning radius. The heavy weight of such vehicles is also important in pavement design and maintenance, as truck volumes influence both the pavement life and design parameters (2). Safety is also affected by LVs: eight percent of fatal vehicle-to-vehicle crashes involved large trucks, although they only accounted for three percent of all registered vehicles and seven percent of total Vehicle Miles Traveled (VMT) (3). Recent studies (4,5) also found that particulate matters (PM) are strongly associated with the onset of myocardial infarction and respiratory symptoms. Heavy duty trucks that use diesel engines are major sources of PM, accounting for 72% of traffic emitted PM (6). PREVIOUS WORK Applying image processing technologies to vehicle detection has been a hot focus of research in Intelligent Transpoatation Systems (ITS) over the last decade. The early video detection research (7) at the University of Minnesota has resulted in the Autoscope video detection systems that are widely used in today s traffic detections and surveillance around the world. Several recent investigations into vehicle classification via computer vision have occurred. Lai et al. (8) demonstrated that accurate vehicle dimension estimation could be performed through the use of a set of coordinate mapping functions. Although they were able to estimate vehicle lengths to within 10% in every instance, their method requires camera calibration in order to map image angles and pixels into real-world dimensions. Similarly, commercially available Video Image Processors (VIPs), such as the VideoTrack system developed by Peek Traffic Inc. METHODOLOGY In order to satisfy the requirements for real-time data collection, the complexity of the approach has to be balanced against its effectiveness. Some pattern recognition and model-matching algorithms (17) can not be executed for real-time detection due to their over-expensive computational cost. A background-based approach that requires less computational work is therefore employed to meet the practical needs. Without complex calibration processes, several simple yet effective algorithms are integrated to handle problems frequently encountered in video-based traffic data collection, such as slight camera vibrations and shadow removal, to enhance the overall system performance. This section describes the major algorithms of this computer vision-based vehicle detection and classification approach. Before presenting the details of each algorithm, the system is briefly overviewed as follows. |