This paper presents a stereo vision system for the detection and distance computation of a preceding vehicle. It is divided in two major steps. Initially, a stereo vision-based algorithm is used to extract relevant three-dimensional (3-D) features in the scene, these features are investigated further in order to select the ones that belong to vertical objects only and not to the road or background. These 3-D vertical features are then used as a starting point for preceding vehicle detection; by using a symmetry operator, a match against a simplified model of a rear vehicle's shape is performed using a monocular vision-based approach that allows the identification of a preceding vehicle. In addition, using the 3-D information previously extracted, an accurate distance computation is performed.
Abstract-In this paper two different vision based systems for vehicle detection are described and their integration discussed. The first approach is based on the use of a specific model for vehicles and mostly relies on monocular vision. Conversely, the second system is based on the use of stereo vision and allows to refine the coarse results obtained by the former.A preliminary integration of the two systems has been tested on the ARGO experimental vehicle and some remarks about reliability and robustness are also included.
Abstract-This paper presents a stereo vision system for vehicle detection. It has been conceived as the integration of two different subsystems. Initially a stereo vision based system is used to recover the most relevant 3D features in the scene; due to the algorithm's generality, all the vertical features are extracted as potentially belonging to a vehicle in front of the vision system. This list of significant patterns is fed to a second subsystem based on monocular vision; it processes the list computing a match with a general model of a vehicle based on symmetry and shape, thus allowing the identification of the sole characteristics belonging to a vehicle.The system presented in this work derives from the integration of the research work developed by the University of Parma (Italy) and I.N.S.A. of Rouen (France). The two subsystems have been integrated into the GOLD software and are currently under testing using the ARGO experimental vehicle.
In: International Conference on Intelligent Transportation Systems 2010International audienceThe localization of intelligent vehicle is an important research topic in the field of intelligent transportation systems. This paper proposes a new vehicle localization method by fusing mono-camera, low-cost GPS and map data. The basic idea is: a possible position range of the vehicle is determined by fusing low-cost GPS output and the map data; Lateral spatial information for high-accuracy localization is provided frequently by vision based lane detection module; both longitudinal and lateral spatial information for high accuracy localization is provided by vision based traffic sign detection module. The proposed method is economically feasible, is reliable and does not need any construction work or change on current traffic environments. Simulation based experiments have shown the effectiveness of the proposed method in achieving high-accuracy localization result (accuracy of centimeter-level) and have also shown apparent better performance of the proposed method over the performance of former method which is similar to the proposed metho
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