The Experts below are selected from a list of 29079 Experts worldwide ranked by ideXlab platform
D U Zhaobin - One of the best experts on this subject based on the ideXlab platform.
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method of image Perspective Transform based on double vanishing point
2009Co-Authors: D U ZhaobinAbstract:Precise location of object in monitor image is based on the Transformation image coordinate and world coordinate.By the reasons of practical application such as allocation of monitor,the parameter of the Transformation is difficult to get.In this paper,a method of Perspective Transform based on double vanishing point is proposed,which is able to Transform image coordinate into world coordinate by analyzing the locations of four control points.Experimental results show the method is reliable and valid.
Xiaoqing Ding - One of the best experts on this subject based on the ideXlab platform.
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ROI Perspective Transform based road marking detection and recognition
2012Co-Authors: Shengjin Wang, Xiaoqing DingAbstract:Road marking detection and recognition play an important role in driving assistance system and intelligent transportation system. We proposed a method for road marking recognition which can be applied to highway and city road based on ROI Perspective Transform. If we can detect the lane lines on the road, we determine the trapezoid ROI where road marking would show up with the guide of detected lane lines. Then the ROI is Transformed to square by inverse Perspective Transform for accurate road marking detection and recognition. If there are no legible lane lines that we can take use of, we estimate the ROI according to experience and adopt the method of connected domain analysis to extract target. Template matching is used for recognition of markings. Overall, the metrics of the method are as follows. First, the detection of lane lines is based on the ATN model which is fast and robust. Second, the detected lines are used as guide for the search of markings, and thus, there is no need to search the makings in the whole image. Third, even there is no lane lines, for example, the lines is worn out or blocked by the vehicle ahead on the city road, the connected domain analysis makes the method suitable for these situations.
Shah, Khan Bahadur - One of the best experts on this subject based on the ideXlab platform.
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Multi road marking detection system for autonomous car using hybrid- based method
2018Co-Authors: Shah, Khan BahadurAbstract:For at least two decades, the development of autonomous systems has led to the development of embedded applications allowing to improve the driving comfort and safety. One of the embedded systems that received great attention is road detection system, that operates using road markings detection algorithm. To date, the issue on detecting road markings under various imaging conditions has not been tackled yet. Generally, the road markings detection is performed on road images extracted from videos that were recorded using a camera, which was placed inside a vehicle at a fixed position. In this thesis, a road markings detection system that tackle the problems of detecting road markings under various weather and illumination conditions is proposed. The proposed system consists of a combination of Inverse Perspective Transform method, an image enhancement method and edge detection method. The Inverse Perspective Transform method was used to convert images, which were extracted from the recorded videos to bird’s-eye view images, while an image enhancement method, namely Contrast Limited Adaptive Histogram Equalization (CLAHE) was used to tackle various illumination conditions and Sobel edge detection method for detecting the road markings. Experimented on Large Variability Road Images database (LVRI) that consists of 22,500 road images, which were extracted from videos recorded around Selangor and Kuala Lumpur and T. Wu dataset that consist of 1208 road images, which were extracted from videos recorded around California, the proposed algorithm performed satisfactorily. With an accuracy of 96.53% using LVRI and 99.33% using the T. Wu datasets, the proposed algorithm able to detect almost all types of road markings. The types of road markings available in the LVRI and T. Wu datasets are forward arrow, left-side arrow, right-side arrow, lanes and signs printed on the road that are under various imaging conditions, including complex background and occlusion. In addition, the proposed algorithm outperformed the algorithm introduced by T. Wu. However, the algorithm has difficulty in detecting road markings painted in soft yellow color. Hence, in future, the algorithm will be improved by incorporating HSI color analysis with the aim of tackling the problem of detecting road markings that are painted in soft yellow color
M M Zhang - One of the best experts on this subject based on the ideXlab platform.
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lane detection algorithm research based on revised Perspective Transform
2009Co-Authors: C N Zhang, T H Tang, X L Kang, M M ZhangAbstract:For a better and more effective way to detect lane and filter noise on road, this paper introduces a way on how to Transform Perspective when dealing with lane detection. An improved algorithm will change Perspective from 3D to 2D and employ a modified Perspective Transform so that we can get a better affection of lane detection. And then, expected values are used to analyze key pixels to acquire accurate lane image point instead of using curve fitting commonly used in lane detection. Both of the creative measures mentioned above will help to acquire precise parameters and lane curves.
Shengjin Wang - One of the best experts on this subject based on the ideXlab platform.
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ROI Perspective Transform based road marking detection and recognition
2012Co-Authors: Shengjin Wang, Xiaoqing DingAbstract:Road marking detection and recognition play an important role in driving assistance system and intelligent transportation system. We proposed a method for road marking recognition which can be applied to highway and city road based on ROI Perspective Transform. If we can detect the lane lines on the road, we determine the trapezoid ROI where road marking would show up with the guide of detected lane lines. Then the ROI is Transformed to square by inverse Perspective Transform for accurate road marking detection and recognition. If there are no legible lane lines that we can take use of, we estimate the ROI according to experience and adopt the method of connected domain analysis to extract target. Template matching is used for recognition of markings. Overall, the metrics of the method are as follows. First, the detection of lane lines is based on the ATN model which is fast and robust. Second, the detected lines are used as guide for the search of markings, and thus, there is no need to search the makings in the whole image. Third, even there is no lane lines, for example, the lines is worn out or blocked by the vehicle ahead on the city road, the connected domain analysis makes the method suitable for these situations.