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Detecting roads using monocular vision is a very challenging task as the detection algorithm must be able to deal with complex real-road scenes. In this paper, we describe an algorithm for general path segmentation. There are three main technical contributions of the approach. First, a path segmentation framework is presented, which formulates road detection as a Bayesian posteriori estimation problem...
In this paper an automated vehicle detection and traffic density estimation algorithm has been developed and validated for very high resolution satellite video data. The algorithm is based on an adaptive background estimation procedure followed by a background subtraction at every video frame. The vehicle detection is performed through a further mathematical morphology and statistical analysis on...
To develop a safe Intelligent Transportation System (ITS) while driving on unpredictable curves or road regions, high precision road segmentation and cover level of forward view estimation for drivers is necessary. Cover level of forward view is defined as the level of difficulty in predicting the dangerousness of road edge or incoming object near road edge especially at a curve due to the obstacles...
Real-time road lane recognition and position estimation algorithm in small vehicle is generally limited by the amount of processing power available. The goal of this research is to develop a light-weight algorithm to recognize a pair of road lane markers using computer vision and to estimate the vehicle position relative to the lane markers. The road lane recognition algorithm uses inverse perspective...
We present a novel approach for vision-based road direction detection for autonomous Unmanned Ground Vehicles (UGVs). The proposed method utilizes only monocular vision information similar to human perception to detect road directions with respect to the vehicle. The algorithm searches for a global feature of the roads due to perspective projection (so-called vanishing point) to distinguish road directions...
Most of the driver assistance systems do not produce accurate results in poor weather conditions. Poor visibility is considered to be a main reason for accidents. The researchers have paid attention on developing various driver assistance systems in order to assure road safety. Image degradation is a severe problem in computer vision applications such as driver assistance systems, terrain classification...
We present a system to perform video analysis in the context of traffic surveillance's application. A training step is performed to estimate the scene's geometry and global information about the motion that occurs in the scene. Lanes boundaries, depth and motion information given by the initialization step are used to assist the vehicles' segmentation and to correct eventual errors.
In this paper we describe a novel approach to autonomous dirt road following. The algorithm is able to recognize highly curved roads in cluttered color images quite often appearing in offroad scenarios. To cope with large curvatures we apply gaze control and model the road using two different clothoid segments. A Particle Filter incorporating edge and color intensity information is used to simultaneously...
This study aims to extract traffic density through traffic monitoring camera images. To this end, it uses Kalman filter based background estimation, which can efficiently adapt to environmental factors such as light change. The difference between the incoming image and the calculated background was subjected to the proposed filters and the vehicles in the foreground were marked. The binary image representing...
In an embedded system, the hardware resources are limited. In order to obtain such traffic information as traffic volume and vehicle speed in an embedded system, a series of efficient video processing algorithms and optimization techniques are proposed. The key algorithm to detect vehicles is the background subtraction method, in which the approximated median filter is applied to obtain the simulated...
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