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A new approach for the detection of the road surface and obstacles is presented. The 3D data from dense stereo is transformed into a rectangular elevation map. A quadratic road surface model is first fitted, by a RANSAC approach, to the region in front of the ego vehicle. This primary solution is then refined by a region growing-like process, driven by the(More)
This paper describes an approach for pedestrian detection in infrared images. The developed system has been implemented on an experimental vehicle equipped with an infrared camera and preliminarily tested in different situations. It is based on the localization of warm symmetrical objects with specific size and aspect ratio; since also road infrastructures(More)
The urban driving environment is a complex and demanding one, requiring increasingly complex sensors for the driving assistance systems. These sensors must be able to analyze the complex scene and extract all the relevant information, while keeping the response time as low as possible. The sensor presented in this paper answers to the requirements of the(More)
An approach for the detection of straight and curved curbs (border of relevant traffic isles, sidewalks, etc) is presented, in the context of urban driving assistance systems. A rectangular elevation map is built from 3D dense stereo data. Edge detection is applied to the elevation map in order to highlight height variations. We propose a method to reduce(More)
This paper presents the improvements of a system for pedestrian detection in infrared images. The system is based on a multi-resolution localization of warm symmetrical objects with specific size and aspect ratio; the multi-resolution approach allows to detect both close and far pedestrians. A match against a set of 3D models encoding human shape's(More)
This paper presents the improvements of a system for pedestrian detection in infrared images. The system is based on a multi-resolution localization of warm symmetrical objects with specific size and aspect ratio; the multi-resolution approach allows to detect both close and far pedestrians. A match against a set of 3D models encoding human shape’s(More)
This paper presents an algorithm for tracking the cuboids generated from grouping the 3D points obtained through stereovision. The solution described in the paper takes into consideration the particularities of the scenario and of the sensor, and brings considerable improvement in all the phases of tracking: initialization, prediction, measurement and(More)
This paper presents a lane detection system that combines stereovision-specific techniques with grayscale image processing for maximizing the robustness and applicability against the difficult conditions of the urban environment. The lane marking features are extracted using a fast and robust dark-light-dark transition detector that's aware of the(More)
This paper proposes a new approach for a vehicle based pedestrian detection and classification system. The pedestrian detection is performed based on the 3D data by generating a density map. Pedestrian classification uses a pattern matching approach and exploits both 2D image information and 3D dense stereo information. Because 3D information accuracy does(More)
This paper describes a modular tracking system designed to improve the performance of a pedestrian detector. The tracking system consists of two modules, a labeler and a predictor. The former associates a tracking identifier to each pedestrian, keeping memory of the past history; this is achieved by merging the detector and predictor outputs combined with(More)