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Video sensors constitute a great innovation in the automotive sector and road safety as they contribute to the development of driver assistance systems. These video systems use image processing techniques to inform drivers of impending dangers. One such development is the Lane Departure Warning System (LDWS) which play a key role in the prevention from accidents. The main function of this system is...
Lane departure and forward collision detection plays an important role in autonomous driving and commercial driver-assistance systems. This paper presents an integrative approach to vision-based lane departure detection which aims to be as simple as possible to enable the real-time computation while being able to adapt to a variety of highway and urban scenarios on different weather conditions. In...
Several vehicle detection methods in urban traffic scenes, such as vehicle detection method based on symmetrical features, vehicle detection method based on license plate, vehicle detection method based on Gabor features and Support Vector Machines (SVM), and vehicle detection method based on Haar-like features and AdaBoost classifier, are comparatively used in this paper. The theoretical analysis...
Computation of Visual Rear Ground Clearance of vehicles is an important computer vision application. This problem is challenging as the road and vehicle rear bumper may have subtle appearance differences, vehicle motion is on uneven surfaces and there are real-time considerations. In this paper a method is presented to compute the Visual Rear Ground Clearance of a vehicle from its rear view video...
In this paper we present a new lane markers detection and estimation algorithm aiming to improve lane detection methods. We first estimate the area of lane marking using the profile of the lane estimation in a confidence map. After that a fitting method is applied to improve the lane marker detection accuracy. To track our lane markers over time and make the association between two iteration, we use...
This paper focuses on the detection of pedestrian crossing intention to improve the situation awareness for autonomous driving in urban environments. A new definition of pedestrian crossing intention is discussed, which allows self-driving vehicles to identify pedestrians, whose intended actions are relevant for the own behavior planning, at an early stage. We propose a context-based feature descriptor...
We extract 3D curb from video sequence, using a single camera equipped with fish-eye lens and located at the front/rear of the vehicle. The challenge in extracting curbs from images lies in their small size and their lack of texture. We show that by appropriately exploiting appearance features, 3D geometry, and temporal information, one can reliably detect and localize the curbs in the 3D scene. The...
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 the field of intelligent vehicle systems (IVS), color and edge of lane markings are important features for vision-based applications. This paper proposes a method to detect lane marking based on a fusion approach which combine color and edge lane marking information. Firstly, by knowing the vehicle speed the road surface region of interest is extracted using the typical stopping distance. Secondly,...
Road marking is a key visual cue for driving in structured environments like highways and urban roads. Road marking detection plays an important role in advanced driver assistant systems and autonomous driving. Robust road marking detection is challenging for the variation of road scenes, the degradation of the markings and the changes of the illumination. Traditional algorithms mainly use the grayscale...
In order to meet the requirements of real time and effectiveness, a multi-feature front vehicle detection algorithm based on video image is proposed. Firstly, the proposed algorithm uses the edge detector algorithm based on wavelet transform to obtain the road mark for reducing the detection region. Secondly, the threshold segmentation algorithm based on weight factor is used for image segmentation...
Fog is a natural and meteorological phenomenon that seems to be very dangerous for road driving. In its presence, the driver has a high perturbation in his field of view and must redouble vigilance. Therefore, it's primary to detect its presence to be able to adapt any advanced driver assistance system according to the density of fog. In this paper, we present a new local approach for detecting daytime...
The paper is focused on the description of modern image processing methods within vehicles with orientation on Lane Departure Warning Systems (LDWS), which are a part of modern Cooperative-Intelligent Transportation Systems (C-ITS). A solution is described for searching of horizontal traffic signs using a segmentation method based on Hough transform. The practical part states the results of software...
Lane detection is a critical step in advanced driver assistance systems (ADAS). The detected lane information is used by later modules of warning and controlling the differential brake and steering angle. Here we propose an efficient algorithm for detecting accurate lane inbounds under varying illumination and road conditions like curvy, straight and dashed lane markings, deterministically. The current...
We present a robust real-time vision-based system for vehicle tracking and categorization, developed for traffic flow surveillance. We propose a robust segmentation algorithm that detects foreground pixels corresponding to moving vehicles. Experimental results based on four large datasets show that our method can count and classify vehicles with a high level of performance (more than 98%).
Advanced Driving Assistant Systems, intelligent and autonomous vehicles are promising solutions to enhance road safety, traffic issues and passengers' comfort. Such applications require advanced computer vision algorithms that demand powerful computers with high-speed processing capabilities. Keeping intelligent vehicles on the road until its destination, in some cases, remains a great challenge,...
For the road edge detection under the structured and semi-structured road environment, we model the road edge using a linear regression and propose an improved RANSAC algorithm to finish the model selection, and it is applied to the fitting and filtering of road edge points extracted from the 2D LIDAR (light detecting and ranging) data. First, road edge points are extracted using the discrete Kalman...
Number plate recognition has been used widely for access control, congestion control, vehicle management, security control and vehicle behavior monitoring system. This study discusses the importance of number plate recognition and its corresponding application in different countries. Various methods for recognizing number plates are reviewed. Most of the systems are able to deliver good recognition...
Many up to date techniques are involved in Intelligent Transportation System (ITS). As an essential part of ITS, License Plate Recognition (LPR) is widely studied. Among the techniques used for LPR, image projection method is testified to be an effective means in obtaining image feature in practice. However, image projection method could not solve all the problems in image identification. For instance,...
In this paper, we propose a geometric framework for stop sign detection based on polylines. We first propose a scheme for the extraction of 1-piece and 2-piece polylines from connected components of edge pixels. This scheme contains three basic steps: i) dominant point extraction, ii) linearity verification, and iii) partitioning. We then propose a polyline-based framework for stop sign detection...
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