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We present a technique used for frontal collision warning in an intelligent vehicle. The system uses an image processing algorithm to detect taillights (red brake lights) of vehicles driving at night time. The area of red lights, the size of the largest brake lights, and the vehicle's speed are used to compute the rear-end collision risk, which can be used as an indicator to danger that potentially...
This paper presents a method for triangular and rectangular shapes detection in a road sign recognition system based on a three step algorithm: color segmentation, shape detection and neural network classification. The shape detector is based on the evaluation of the Sobel edges and Hough images in a region of interest detected by the color-based stage. During the tests performed the shape detector...
This paper presents a road signs detection, recognition and tracking system based on multi-cues hybrid. In detection stage, the color and gradient cues are used to segment the interesting regions, and the corner and geometrical cues are used to detect the signs. A pseudo RGB-HSI conversion method without the need of nonlinear transformation is presented for color extraction. In recognition stage,...
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...
This paper describes a detection system of traffic sign for use in moving cars. Input image captured from color CCD camera is converted in HSV or RGB color space. In color space are done preprocessing actions, to determine the position of traffic signs. Image segment from this position is further tested for class of traffic sign, which means: features are extracted and used as input in to image classification...
This paper reports a method for acquiring the prior probability of human existence by using past human trajectories and the color of an image. The priors play important roles in human detection as well as in scene understanding. The proposed method is based on the assumption that a person can exist again in an area where he/she existed in the past. In order to acquire the priors efficiently, a high...
Road sign detection is one of the major concerned topics in the field of driving safety and intelligent vehicle. In this paper, a novel model based on Color Barycenters Hexagon (CBH) is proposed and used to detect road sign usefully. In CBH model, full color images are calculated the color barycenters and get the barycenters region, then automatic select the idea threshold curves to separate the region...
Moving vehicle recognition and tracking is the key technology in the intelligent traffic monitoring system. For the shortcomings and deficiencies of the frame-subtraction method, a binary discrete wavelet transforms based moving object recognition algorithm is put forward, which directly detects moving vehicles in the binary discrete wavelet transforms domain. For the shortages of RGB or HSV color...
In this paper we have presented colored traffic signage detection using correlation techniques based on joint transform correlator(JTC). The performance of proposed technique has been evaluated when noisy target images amalgated with clutterd background are fed to the correlator. The effect of scale variation of the target images has also been discussed.
This paper describes a method for classifying road signs based on a single color camera mounted on a moving vehicle. The main focus will be on the final neural network based classification stage of the candidates provided by an existing traffic sign detection algorithm. Great attention is paid to image preprocessing in order to provide a more simple and clear input to the network: candidate color...
This paper presents an integrated approach to robust analysis of road area images in front of the car from a single color camera. In order to get more information from the image source, we build a three-level data fusion based on Dempster-Shaferpsilas decision theory. During the first level, we separate the input image into perspective view and birdpsilas view and recognize small road patches from...
Here we propose a complete system for robust detection and recognition of the current speed sign restrictions from a moving road vehicle. This approach includes the detection and recognition of both numerical limit and national limit (cancellation) signs with the addition of automatic vehicle turn detection. The system utilizes both RANSAC-based colour-shape detection of speed limit signs and neural...
In this paper we present a novel method for parsing aerial images with a hierarchical and contextual model learned in a statistical framework. We learn hierarchies at the scene and object levels to handle the difficult task of representing scene elements at different scales and add contextual constraints to resolve ambiguities in the scene interpretation. This allows the model to rule out inconsistent...
In this paper, a method is presented for road surface segmenting in unstructured roads, which have no lane marking. When the original color images are preprocessed using mathematical morphology, binary image is acquired by a threshold in color space. Next, as the deformable template matches in the binary image, some points of road centerlines can be gained. Then, the road surface can be segmented...
In this research the problem of the detection and classification of road bridge sign has been faced, in particular, at butterfly bridge configuration. The first step concerns the robust identification of the rectangular sign, through the optical flow analysis. The second step concerns rectangular sign detection based on searching gray level discontinuity on the image and Hough transform. The classification...
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