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This paper aims at presenting some methods of representing image's features that help detect and classify vehicles from video. Proposed methods include: Method of representing shape, contour of vehicle or block of vehicle that can be classified. Paramaters of the Image's length in combination with parmaters of visual length of object that can used to classify object type or separate object. Use genaral...
In this paper, we propose an accurate and real-time positioning method for intelligent road vehicles in urban environments. The proposed method uses a robust lane marking detection algorithm, as well as an efficient shape registration algorithm between the detected lane markings and a GPS based road shape prior, to improve the robustness and accuracy of global localization of a road vehicle. We exploit...
Traffic sign detection and recognition is one of the important fields in the intelligent transportation system, and is expected to provide information on traffic signs and guide vehicles during driving. Traffic sign segmentation is the first stage in traffic sign recognition system, and segmentation results influence the recognition results. This paper presents an efficient method for traffic sign...
The goal of the project is to design intelligent and robust image-processing and augmented-reality algorithms for driver assistance and enhanced vehicular safety. In particular, the focuses were two-fold: (1) realizing the abilities to identify and localize in a vehicle''s on-board video the sweeping windshield wipers during raining days and (2) designing and implementing an in-painting technique...
As an important component of the driver assistance system or autonomous vehicle, traffic sign detection can provide drivers or vehicles with safety and alert information about the road. Most existing methods for traffic sign detection only focus on one or several categories of signs while there are various signs in the real world. This paper proposes a biologically-inspired method for detecting almost...
In recent years, a lot of researches on traffic sign detection and recognition have been done. But most of them were tested under restricted conditions such as camera with high resolution and sensitivity, highway environment or road side having a lot of trees and very few distracting objects. In this paper, we present a fast and robust traffic sign detection system including two main stages: segmentation...
Nowadays, many vehicles are equipped with a vehicle borne camera system for monitoring drivers' behavior, accident investigation, road environment assessment, and vehicle safety design. Huge amount of video data is being recorded daily. Analyzing and interpreting these data in an efficient way has become a non-trivial task. As an index of video for quick browsing, this work maps the video into a temporal...
Vision based road detection is a key technique in Autonomous driving and Advance Drive Assistance System. We propose an approach for vision-based road detection which exploits road shape prior, color consistence representation and discrimination analysis. Firstly, road shape prior probability map is constructed from available road images which have been annotated by matching and classifying of road...
Vehicular ad hoc networks have emerged as a promising area of research in academic fields. However, to design a realistic coverage algorithm for vehicular networks presents a challenge due to the irregularity of the service area, assorted mobility patterns, and resource constraints. In order to resolve these problems, this paper proposes a genetic algorithm-based sparse coverage with statistical analysis,...
Road sign recognition is considered to be one of the most fascinating and interesting field of research in intelligent vehicle and machine learning. Road signs are typically placed either by the roadside or above roads. They provide important information in order to make driving safer and easier. This paper proposes an algorithm that recognizes Bangla road sign with a better percentage. The algorithm...
In this paper, a new Advanced Driver Assistant System (ADAS) system for LED traffic sign detection algorithm using rectangle shape based on a windowed hough transform and feature based optimization was presented. We used two character to detect the LED traffic sign. One is LED traffic sign have rectangle shape, another is intensity feature of LED traffic sign. After extracting the candidates of LED...
In this paper, we present a robust approach of automatic detection and recognition of road signs in national roads, starting from the images resulting from a video stream taken by a camera embarked on a vehicle. Our approach is composed of three main phases: the first phase is to extract video stream images containing a circle or a triangle. This extraction is performed respectively by Hough transformation...
A control algorithm for smart car auto-tracing with visual is designed in this paper. The smart car is structured with a DC motor for running driver and a steer motor for turning control, which can run along a black line at a high speed. A control strategy for smart car turning is proposed based on the road visual, through the image of road, the smart car can tracking along the road and the running...
Road detection is a crucial problem for autonomous navigation system (ANS) and advance driver-assistance system (ADAS). In this paper, we propose a hierarchical road detection method for robust road detection in challenging scenarios. Given an on-board road image, we first train a Gaussian mixture model (GMM) to obtain road probability density map (RPDM), and next oversegment the image into superpixels...
Texts on road signs contain important information which is quite useful for potential applications. We proposed a robust method for detecting road sign text from urban street scenes under different weather conditions. First, color Segmentation and morphological operations are employed to obtain candidate regions, and contours of candidate regions are mainly concern. Then, a linear support vector machine...
In this paper, we study the problem of detecting and tracking multiple objects of various types in outdoor urban traffic scenes. This problem is especially challenging due to the large variation of road user appearances. To handle that variation, our system uses background subtraction to detect moving objects. In order to build the object tracks, an object model is built and updated through time inside...
The aim of the project is to detect and recognize traffic signs in video sequences recorded by an onboard vehicle camera. Traffic Sign Recognition (TSR) is used to regulate traffic signs, warn a driver, and command or prohibit certain actions. A fast real-time and robust automatic traffic sign detection and recognition can support and disburden the driver and significantly increase driving safety...
In this work, we propose a light method for the recognition of traffic signs. This new approach involves three steps which are the recognition of the shape of the border, the border color, and recognition of the pictogram. To extract the color, the RGB color representation is used. The method of profile projection (outermost point method) is used to extract the feature vector associated with the border...
Analysis of dynamic brain imaging data from EEG, MEG or fMRI requires a common temporal context to enable meta-analysis and data mining across experiments. However, there is no standardized method of annotating events, even from laboratory experiments in controlled settings, and the event-rich environments of real-world brain imaging present a still greater annotation challenge. We have developed...
This paper proposes a fast road signs recognition framework with shape filtering and contour features. We observe the standard road signs and construct two basic feature tables for recognition processing. In road driving images, we transfer the RGB color domain to HSV for the red regions detection firstly. And we obtain candidate regions and decide the kinds of shapes by eliminating the redundant...
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