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David Vazquez, David Geronimo, & Antonio Lopez. (2009). The effect of the distance in pedestrian detection (Vol. 149). Master's thesis, , .
Abstract: Pedestrian accidents are one of the leading preventable causes of death. In order to reduce the number of accidents, in the last decade the pedestrian protection systems have been introduced, a special type of advanced driver assistance systems, in witch an on-board camera explores the road ahead for possible collisions with pedestrians in order to warn the driver or perform braking actions. As a result of the variability of the appearance, pose and size, pedestrian detection is a very challenging task. So many techniques, models and features have been proposed to solve the problem. As the appearance of pedestrians varies signi cantly as a function of distance, a system based on multiple classi ers specialized on diferent depths is likely to improve the overall performance with respect to a typical system based on a general detector. Accordingly, the main aim of this work is to explore the e ect of the distance in pedestrian detection. We have evaluated three pedestrian detectors (HOG, HAAR and EOH) in two di erent databases (INRIA and Daimler09) for two di erent sizes (small and big). By a extensive set of experiments we answer to questions like which datasets and evaluation methods are the most adequate, which is the best method for each size of the pedestrians and why or how do the method optimum parameters vary with respect to the distance
Keywords: Pedestrian Detection
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David Vazquez, & Antonio Lopez. (2008). Intrusion Classification in Intelligent Video Surveillance Systems.
Abstract: An intelligent video surveillance system (IVS) is a camera-based installation able to process in real-time the images coming from the cameras. The aim is to automatically warn about different events of interest at the moment they happen. Daview system of Davantis is a com mercial example of IVS system. The problems addressed by any IVS system, and so Daview, are so challenging that none IVS system is perfect, thus, they need continuous improvement. Accordingly, this project aims to study different approaches in order to outperform current Daview performance, in particular, we bet for improving its classification core. We present an in deep study of the state of the art on IVS systems, as well as on how Daview works. Based on that knowledge, we propose four possibilities for improving Daview classification capabilities: improve existent classifiers; improve existing classifiers combination; create new classifiers and create new classifier-based architectures. Our main contribution has been the incorporation of state-of-the-art feature selection and machine learning techniques for the classification tasks, a viewpoint not fully addressed in current Daview system. After a comprehensive quantitative evaluation we will see how one of our proposals clearly outperforms the overall performance of current Daview system. In particular the classification core that we finally propose consists in an AdaBoost One-Against-All architecture that uses appearance and motion features that were already present in current Daview system
Keywords: Human detection; Car detection; Intrusion detection
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Lluis Barcelo. (2002). Accurate video mosaicing with moving objects.
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Cristina Cañero, & Petia Radeva. (2002). Predictive (un)distortion model for 3D reconstruction purpouses.
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