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This study describes a method for using a camera to automatically recognize the speed limits on speed-limit signs. This method consists of the following three processes: first (1) a method of detecting the speed-limit signs with a machine learning method utilizing the local binary pattern (LBP) feature quantities as information helpful for identification, then (2) an image processing method using...
Traffic sign recognition (TSR) represents an important feature of advanced driver assistance systems, contributing to the safety of the drivers, pedestrians and vehicles as well. Developing TSR systems requires the use of computer vision techniques, which could be considered fundamental in the field of pattern recognition in general. Despite all the previous works and research that has been achieved,...
In order to reduce the number of accidents caused by the call when the driver was driving, this paper uses the computer vision technology to dectet the behavior of the driver. Based on the constrained local models (CLM) to detect the characteristic changes of the mouth area, combine the HSV color space and the template matching to detect the hand characteristics to judge whether the driver has the...
The traffic sign detection and recognition is an integral part of Advanced Driver Assistance System (ADAS). Traffic signs provide information about the traffic rules, road conditions and route directions and assist the drivers for better and safe driving. Traffic sign detection and recognition system has two main stages: The first stage involves the traffic sign localization and the second stage classifies...
To achieve the goal of frontal vehicle detection in night-driving condition, we propose an effective method to detect the red taillights of vehicles. The challenge is that the taillight images captured with automatic exposure typically are overexposed, which makes red color segmentation often erroneous. Instead of customizing the camera hardware to tackle this problem, we combine morphological and...
Vehicle detection and recognition from aerial imagery provides useful information for local vehicle volume estimation and traffic monitoring. In this paper, we propose a method that accurately detects vehicles in urban environment using a probabilistic classification method followed by a refinement based on object segments. Both classification and segmentation methods make use of coregistered aerial...
In this paper, we propose a vision-based traffic light and arrow detection algorithm for intelligent vehicles. We detect all three traffic light colours along with the arrow direction robustly for varying illuminations and traffic lights. A fine-tuned convolutional neural network is used in an offline phase to localise the traffic light region-of-interest within a given camera image. Given the constrained...
We introduce a new computer vision based system for robust traffic sign recognition and tracking. Such a system presents a vital support for driver assistance in an intelligent automotive. Firstly, a color based segmentation method is applied to generate traffic sign candidate regions. Secondly, the HoG features are extracted to encode the detected traffic signs and then generating the feature vector...
Traffic sign detection and recognition plays an important role in driver assistance system especially in complexity environment. Firstly, RGB image is converted to standardization image only contained 8 colors for reducing computational burden. Only interesting color components are extracted as candidate region for further recognition. Then HOG descriptor is considered as detection characteristic...
Todays, the number of vehicles is rapidly increasing. In parallel, the number of ways and traffic signs have increased. As a result of increased traffic signs, the drivers are expected to learn all the traffic signs and to pay attention to them while driving. A system that can automatically recognize the traffic signs has been need to reduce traffic accidents and to drive more freely. Traffic sign...
This article describes the vertical traffic signs recognition (VTSR) system. In this system the Cambridge optical correlator is used as an image comparator in the recognition stage. In our case, the Cambridge optical correlator compares detected traffic signs with reference traffic signs. One step of the VTSR system is the traffic signs detection process. Traffic signs detection is used to locate...
The traffic sign detection and recognition is necessary for the safety and proper navigation of drivers. Intelligent driver assistance systems have great potential in emerging technologies. This paper presents an efficient algorithm which detects the traffic sign from video based on colour and shape information. Then the auto associative neural networks are performed to recognise the traffic signs...
Traffic signs serve important functions on the road. Drivers can easily determine their directions and vehicle speeds by paying attention to traffic signs. However, it is only natural that sometimes drivers misjudge the position and meaning of traffic signs that they ignore them and in the worst case scenario, got involved in accidents. Therefore, technological improvements allow the development of...
This conceptual paper examines the computer identification of signs called day markers that commonly appear for navigation purposes in a marine channel waterway. Most people are familiar with these signs when navigating in a boat as they appear either as red triangles, green rectangles or diamonds since they aid navigation through the channel. The paper focuses on a proposed classifier using Procrustes...
To deal with road accidents, especially accidents caused by trucks containing dangerous products, the possible solution is to control these vehicles' passage. We aim at developing a software technique confirming that all the entered engines inside a tunnel are securely quitted, to guarantee that no accidents, no breakdowns have occurred inside. To implement such solution, we identify the ingoing and...
This paper introduces a dramatically novel traffic signs recognition (TSR) system that can perform traffic sign detection and tracking simultaneously. The proposed approach utilizes intensity images and the depth images, in parallel, to robustly detect and track traffic signs in real-time. Additionally, we suggest to supplement the ordinary traffic signs with the corresponding quick-response (QR)...
Research in traffic light recognition (TLR) has stagnated compared to related computer vision areas, such as pedestrian detection and and traffic sign recognition. We focus on the detection sub-problem, since this is the most challenging problem and solving this is the key to a successful TLR system. This is done by looking at four detectors from different author groups and their reported results...
While much work in the domain of traffic lights recognition is invested in the detection of traffic lights, classification of their exact state (including color phase and possible arrow pictogram) is often neglected. In this paper, we propose a robust approach for efficient video-based classification of said state with particular attention to the displayed pictogram and an additional ability to reject...
This paper proposes the control design and implementation of an intelligent vehicle combined with robotic manipulator and computer vision. It can be roughly divided into three main directions for the design of intelligent vehicle, including path planning of mobile vehicle, position control of robot arm system and appropriate applications of image recognition. For the consideration of path planning...
We present a small database of “noisy” traffic signs in cluttered urban environments that exhibit various forms of degradation, including vandalism and fading (discoloration). The database contains five types of international traffic signs that allow differentiation by means of color and shape, and it has been collected in two cities in Greece. We further present a baseline system for detecting and...
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