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This paper presents a new vision-based vehicle detection method for Forward Collision Warning System (FCWS) at nighttime. Also, lane detection is performed for assistance. To effectively extract the bright objects of interest, an essential image preprocessing including the tone mapping, contrast enhancement and adaptive binaryzation is applied in the nighttime road scenes. The characteristics of taillights...
This paper proposes an new algrithem framework for video based vehicle âtailgate' behavior detection in urban road junction. Based on the road traffic regualtion about the illegal parking behavior definition, the author proposes traffic lights signal monitor, vehicle tracking and road conjestion detection algrithems in the real time video analyse. The measured parameters including vehicles' trojactory,...
Road detection is the most fundamental part of autonomous vehicles. Noises caused by shadows and vehicles on the road have a great negative impact on the road detection. Our method improves the performance under the noisy environment by taking advantage of color information to determine the road curvature. A novel geometrical method is proposed in this paper to select the most matched curvature. A...
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 present a new methodology for detecting lane markers that is able to withstand many challenging situations like scattered shadows, illumination changes, and presence of neighboring vehicles to name a few. At first, the input image undergoes a perspective removal followed by a color space conversion. Then, the core elements consisting of template matching, lane region merging, and...
In this paper, we propose a system that automatically detects and recognizes road signs found in the United States, in real time or close to real-time. The proposed system has application to intelligent autonomous unmanned vehicles for urban surveillance and rescue. It is a multi-layered hierarchical scheme composed of 3 parts: road sign color segmentation, shape recognition, and classification. The...
In order to reduce accident at traffic intersections during day and night, the algorithm of traffic lights detection which is applied in a vehicle driver assistance system is designed by using the image processing technology. The system of traffic light detection includes three parts: a CCD camera, an image acquisition card, and a PC. Based on RGB color space, the algorithm extracts red, green, and...
Traffic flow, analysis and control is gaining high relevance, as the number of circulating vehicles continuously increases. This article proposes a computer vision based platform, which automatically detects vehicles in order to infer the traffic conditions. The developed real time detection algorithm is based on a dual background subtraction technique, incorporating the one known has Codebook and...
Various image processing techniques and geometric models have been applied in vision based lane detection subsystems of intelligent vehicles and Advanced Driver Assistance Systems (ADAS). However, challenging conditions such as strong shadows, occlusions, eroded markings, high curvatures are ongoing issues in this topic. In this paper, a novel lane extraction method based on symmetrical local threshold...
In this paper, we present a new fast method for matching stereo images acquired by a stereo sensor embedded in a moving vehicle. The method consists in exploiting the matching results obtained in one stereo pair (frame) for computing the disparity map of the following stereo pair. This can be achieved by finding a temporal relationship, which we named association, between consecutive frames. The disparity...
Road detection is a vital task for the development of autonomous vehicles. The knowledge of the free road surface ahead of the target vehicle can be used for autonomous driving, road departure warning, as well as to support advanced driver assistance systems like vehicle or pedestrian detection. Using vision to detect the road has several advantages in front of other sensors: richness of features,...
We propose a new algorithm for detecting triangles of uniform color within natural images, subject to certain limitations which will be addressed in forthcoming publications. The main application area in our mind is autonomous vehicle control or driver assistance subsystems, where our algorithm can be employed to detect triangle-shaped fiducials or road signs. The algorithm can easily be extended...
Vision-based road detection is very challenging since the road is in an outdoor scenario imaged from a mobile platform. In this paper, a new top-down road detection algorithm is proposed. The method is based on scene (road) classification which provides the probability that an image contains certain type of road geometry (straight, left/right curve, etc.). During the training of the classifier a road...
This paper proposes an effective lane detection and tracking method using statistical modeling of lane color and edge-orientation in the image sequence. At first, we will address some problem of classifying a pixel into two classes(lane or background) and detecting one exact lane. Generally, the probability of a pixel classification error conditioned on the distinctive feature vector can be decreased...
This paper presents a robust real-time rear-view camera based object detection algorithm for backup aid and parking assist applications. The system is capable of handling the challenges of stationary as well as moving objects in rear view of the host vehicle, utilizing a single car-mounted rear-view fish-eye camera. A motion-based and edge-based object detection algorithm was developed in order to...
Due to the recent progress in computer vision to interpret images and sequence of images, the video camera is a promising sensor for traffic monitoring and traffic surveillance at low cost. This paper focuses on the detection and tracking of multiple vehicles present in the field of view of a camera. Until now, the vehicle detection has been mainly performed by the widely used technique called background...
Accurate lane detection in real-time is a critical task in autonomous vehicle guidance and lane departure warning for driver assistance. Existing vision-based approaches rely mostly on some analysis of the spatial gradient of the image. However, if the road structure is not regular and well delimited, edges may not be easy to extract and other features must be employed. This paper evaluates the use...
Real-time image processing is a difficult work for traffic video monitoring. This paper proposed a method to detect and track vehicles on highway based on airship video and therefore calculate traffic parameters in real-time. A blocking road extraction was performed to determine the ROI, and automatically calculate the tilt of the road which contributes to vehicles detection. A lane marks registration...
Road detection is a crucial part of autonomous driving system. Most of the methods proposed nowadays only achieve reliable results in relatively clean environments. In this paper, we combine edge detection with road area extraction to solve this problem. Our method works well even on noisy campus road whose boundaries are blurred with sidewalks and surface is often covered with unbalanced sunlight...
Long-range detection of road surface in a sequence of images from a front camera aboard a vehicle is known as an unsolved problem. We propose an algorithm using a single camera and based on color segmentation which has interesting performance and which is stable along the sequence whatever its length. It is an off-line algorithm which makes good use of current and successive images to build reliable...
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