Chen, ZezhiEllis, TimVelastin Carroza, Sergio Alejandro2018-04-042018-04-042016-04-28Chen, Z., Ellis, T., Velastin, S.A. (2016).Vision-based traffic surveys in urban environments. Journal of Electronic Imaging, 25(5), 0512061017-9909https://hdl.handle.net/10016/26560This paper presents a state-of-the-art, vision-based vehicle detection and type classification to perform traffic surveys from a roadside closed-circuit television camera. Vehicles are detected using background subtraction based on a Gaussian mixture model that can cope with vehicles that become stationary over a significant period of time. Vehicle silhouettes are described using a combination of shape and appearance features using an intensity-based pyramid histogram of orientation gradients (HOG). Classification is performed using a support vector machine, which is trained on a small set of hand-labeled silhouette exemplars. These exemplars are identified using a model-based preclassifier that utilizes calibrated images mapped by Google Earth to provide accurately surveyed scene geometry matched to visible image landmarks. Kalman filters track the vehicles to enable classification by majority voting over several consecutive frames. The system counts vehicles and separates them into four categories: car, van, bus, and motorcycle (including bicycles). Experiments with real-world data have been undertaken to evaluate system performance and vehicle detection rates of 96.45% and classification accuracy of 95.70% have been achieved on this data.22application/pdfeng© 2016 Society of Photo Optical Instrumentation Engineers (SPIE) and IS&T. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, or modification of the contents of the publication are prohibitedbackground subtractiongaussian mixture modelvehicle detectionvehicle classificationvehicle trackingresearch articleInformáticahttps://doi.org/10.1117/1.JEI.25.5.051206open access5Journal of Electronic Imaging25AR/0000021045