Vision based road lane detection system for vehicles guidance

Driver support system is one of the most important feature of the modern vehicles to ensure driver safety and decrease vehicle accident on roads. Apparently, the road lane detection or road boundaries detection is the complex and most challenging tasks. It is includes the localization of the road and the determination of the relative position between vehicle and road. A vision system using on-board camera looking outwards from the windshield is presented in this paper. The system acquires the front view using a camera mounted on the vehicle and detects the lanes by applying few processes. The lanes are extracted using Hough transform through a pair of hyperbolas which are fitted to the edges of the lanes. The proposed lane detection system can be applied on both painted and unpainted roads as well as curved and straight road in different weather conditions. The proposed system does not require any extra information such as lane width, time to lane crossing and offset between the center of the lanes. In addition, camera calibration and coordinate transformation are also not required. The system was investigated under various situations of changing illumination, and shadows effects in various road types without speed limits. The system has demonstrated a robust performance for detecting the road lanes under different conditions

Vision-based traffic surveys in urban environments

This paper presents a state-of-the-art, vision-based vehicle detection and type classification to perform traffic surveys from a roadside closed-circuit television camera. Vehicles are detected using background subtraction based on a Gaussian mixture model that can cope with vehicles that become stationary over a significant period of time. Vehicle silhouettes are described using a combination of shape and appearance features using an intensity-based pyramid histogram of orientation gradients (HOG). Classification is performed using a support vector machine, which is trained on a small set of hand-labeled silhouette exemplars. These exemplars are identified using a model-based preclassifier that utilizes calibrated images mapped by Google Earth to provide accurately surveyed scene geometry matched to visible image landmarks. Kalman filters track the vehicles to enable classification by majority voting over several consecutive frames. The system counts vehicles and separates them into four categories: car, van, bus, and motorcycle (including bicycles). Experiments with real-world data have been undertaken to evaluate system performance and vehicle detection rates of 96.45% and classification accuracy of 95.70% have been achieved on this data.The authors gratefully acknowledge the Royal Borough of Kingston for providing the video data. S.A. Velastin is grateful to funding received from the Universidad Carlos III de Madrid, the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement nº 600371, el Ministerio de Economía y Competitividad (COFUND2013-51509) and Banco Santander

Robust Vehicle Detection and Distance Estimation Under Challenging Lighting Conditions

Avoiding high computational costs and calibration issues involved in stereo-vision-based algorithms, this paper proposes real-time monocular-vision-based techniques for simultaneous vehicle detection and inter-vehicle distance estimation, in which the performance and robustness of the system remain competitive, even for highly challenging benchmark datasets. This paper develops a collision warning system by detecting vehicles ahead and, by identifying safety distances to assist a distracted driver, prior to occurrence of an imminent crash. We introduce adaptive global Haar-like features for vehicle detection, tail-light segmentation, virtual symmetry detection, intervehicle distance estimation, as well as an efficient single-sensor multifeature fusion technique to enhance the accuracy and robustness of our algorithm. The proposed algorithm is able to detect vehicles ahead at both day or night and also for short- and long-range distances. Experimental results under various weather and lighting conditions (including sunny, rainy, foggy, or snowy) show that the proposed algorithm outperforms state-of-the-art algorithms

Overview of Environment Perception for Intelligent Vehicles

This paper presents a comprehensive literature review on environment perception for intelligent vehicles. The state-of-the-art algorithms and modeling methods for intelligent vehicles are given, with a summary of their pros and cons. A special attention is paid to methods for lane and road detection, traffic sign recognition, vehicle tracking, behavior analysis, and scene understanding. In addition, we provide information about datasets, common performance analysis, and perspectives on future research directions in this area

A vision-based method for on-road truck height measurement in proactive prevention of collision with overpasses and tunnels

This is the accepted manuscript. The final version is available from Elsevier at http://www.sciencedirect.com/science/article/pii/S0926580514002167.Over-height trucks are continuously striking low clearance overpasses and tunnels. This has led to significant damage, fatalities, and inconvenience to the public. Smart systems can automatically detect and warn oversize trucks, and have been introduced to provide the trucks with the opportunity to avoid a collision. However, high cost of implementing these systems remains a bottleneck for their wide adoption. This paper evaluates the feasibility of using computer vision to detect over-height trucks. In the proposed method, video streams are collected from a surveillance camera attached on the overpass/tunnel, and processed to measure truck heights. The height is measured using line detection and blob tracking which locate upper and lower points of a truck in pixel coordinates. The pixel coordinates are then translated into 3D world coordinates. Proof-of-concept experiment results signify the high performance of the proposed method and its potential in achieving cost-effective monitoring of over-height trucks in the transportation system. The limitations and considerations of the method for field implementation are also discussed.This material is based upon work supported by West Virginia University, Myongji University, and University of Cambridge

An fMRI study of parietal cortex involvement in the visual guidance of locomotion

Locomoting through the environment typically involves anticipating impending changes in heading trajectory in addition to maintaining the current direction of travel. We explored the neural systems involved in the “far road” and “near road” mechanisms proposed by Land and Horwood (1995) using simulated forward or backward travel where participants were required to gauge their current direction of travel (rather than directly control it). During forward egomotion, the distant road edges provided future path information, which participants used to improve their heading judgments. During backward egomotion, the road edges did not enhance performance because they no longer provided prospective information. This behavioral dissociation was reflected at the neural level, where only simulated forward travel increased activation in a region of the superior parietal lobe and the medial intraparietal sulcus. Providing only near road information during a forward heading judgment task resulted in activation in the motion complex. We propose a complementary role for the posterior parietal cortex and motion complex in detecting future path information and maintaining current lane positioning, respectively. (PsycINFO Database Record (c) 2010 APA, all rights reserved