Road Recognition Research Articles

Road Recognition in Complex Traffic Situations

This paper describes the algorithms for road edge detection in complex traffic situations by an on-board video camera. The information that comes from the motion of a dynamic scene is used to build a new family of filters called Dynamic Time Filters (DTF). These filters apply morphological opening and closing transformations to the image sequence provided by the camera. This sequence is considered as a 3D image. The DTF-filtered image is segmented by means of the watershed transformation. Two different modes have been implemented for the segmentation. The acquisition mode allows an initial extraction of the current traffic lane. It also produces initial markers which will be used by the second segmentation mode, where an interpolated road/lane model is used to follow and segment the lanes.Some results of the segmentation process are given in complex situations to illustrate the efficiency and robustness of this approach. Information is also given on the real-time performance of the algorithms implemented on a car demonstrator.

Road recognition with a neural network

This paper presents a neural-network model that is capable of recognising roads in colour images of urban and rural road scenes. The network is trained to classify the visible objects in an image, given a set of hand-labelled examples. The two problems of segmentation and recognition are separated through the use of “ideal” segmentations, allowing the performance of the recognition method to be assessed independently of the effects of using an imperfect real segmentation process. The generalisation performance of the method is measured using unseen test data. Results for both on-road and offroad viewpoints are presented. The system is found to recognise correctly approx. 70% of the regions, and approx. 90% of the area of the images

025 Road recognition with a neural network

025 Road recognition with a neural network

Road Recognition with A Neural Network

This paper presents a neural network model that is capable of recognising roads in colour images of urban and rural road scenes. The network is trained to classify the visible objects in an image given a set of hand-labelled examples. The two problems of segmentation and recognition are separated through the use of “ideal” segmentations, allowing the performance of the recognition method to be assessed independently of the effects of using an imperfect real segmentation process. Results for both on-road and off-road viewpoints are presented

The Warp machine on Navlab

The authors review the history of the Carnegie-Mellon Warp machine on Navlab, an autonomous land vehicle, and describe three Navlab vision systems implemented on the Warp machine. They then critically evaluate components of Warp in light of this experience. The Warp machine was used to implement stereo vision for obstacle avoidance and color-based road-following systems. The stereo-vision system was FIDO, which is derived from some of the earliest work in vision-guided robot vehicle navigation. Two color-based road following systems were implemented; one adapted conventional vision techniques to the problem of road recognition and the other used a neural network-based technique to learn road following online. Finally, the authors discuss the value of applications integration with machine development, discuss the limitations of the attached processor model, and give recommendations for future systems.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Algorithm For Automatic Road Recognition On Digitized Map Images

This paper presents an algorithm to detect road lines on digitized map images. This algorithm detects road lines based on object shape (line thickness) and gray level values. The road detection process is accomplished in two steps: road line extraction and road tracking. The road line extraction consists of level slicing, morphological filtering, and connected component analysis. The road tracking routine is capable of connecting broken road lines caused by the overlapping of text labels. The algorithm has been implemented on an IBM PC/AT-based image processing system and applied to various map images.

Road sign recognition and non-recognition

The problem of non-recognition of road signs has many aspects which are of great importance in traffic safety. Considering all signs on a test road and eye-movement technique and recognition rate method, a temporal analysis has been conducted for two techniques of driving: driving with the time necessary to see, read and recognize each type of road sign, and free driving to determine the actual time the driver spends reading these signs. The actual time spent provides recognition rates, totally and partially, and also rates of non-recognition. Many of the factors involved were investigated and the analysis was designed to estimate the effect of these factors separately. For a more practical use of the results, a set of probabilistic models has been estimated to characterize the different distributions of fixation durations. Next, the parameters of these models were used to develop a method for measuring the efficiency-level index of the road sign system.