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In this work we present a method to automatically learn and detect cast shadows on highway surveillance scenarios. The first stage of this method uses a weak classifier as a pre-filter to select possible shadowed pixels in order to learn multi-layered statistical shadow models using a recursive Bayesian learning approach. These models will then be used, by a strong classifier, to correctly distinguish...
For a Driving Assistance System dedicated to intersection safety, knowledge about the structure and position of the intersection is essential, and detecting the painted road signs can greatly improve this knowledge. This paper describes a method for detection, measurement and classification of painted road objects that are typically found in European intersections. The features of the painted objects...
Automatic vehicle detection systems in urban and inter-urban traffic using computer vision are frequently based on background subtraction methods. Moving shadows represent a serious difficulty for these methods, as they will appear as part of the segmented foreground vehicles. Shadow removal algorithms usually rely on exploiting color properties. However, the use of image color information, when available,...
Road segmentation is an essential functionality for supporting advanced driver assistance systems (ADAS) such as road following and vehicle and pedestrian detection. Significant efforts have been made in order to solve this task using vision-based techniques. The major challenge is to deal with lighting variations and the presence of objects on the road surface. In this paper, we propose a new road...
Track selective localization of railway vehicles is a precondition to more efficient logistics, improved security and autonomous driving. Often, satellite based navigation systems are used for localization tasks. However, in many cases, satellite navigation is not available or the sensor information is corrupted. To enhance availability and localization quality new sensors are needed. In this work...
Detection and classification of traffic signs is one of the most studied Advanced Driver Assistance Systems (ADAS) and some solutions are already installed in vehicles. Nevertheless these systems still have room for improvement in terms of speed and performance. When driving at high speed, warning systems require very fast processing of the video stream in order to lose as few frames as possible and...
This paper presents the results of an all-day-long pedestrian classification system based on an AdaBoost cascade meta-algorithm. The underlying idea is to use a Haar-features-based AdaBoost together with an ad-hoc-features-based AdaBoost system in order to reach a better pedestrian classification. A specific night-time pedestrian classification is developed in order to obtain a system that can be...
Identification of vehicles for security reasons has lately attracted much scientific and commercial attention. In certain areas, such as government buildings, army camps or country borders, the vehicles are inspected before allowed to enter. As this inspection needs to be thorough, it is a rather time-consuming process. To address this issue, this paper proposes a combination of distinct computer...
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