A lane-departure identification based on LBPE, Hough transform, and linear regression

Lee, Joon Woong; Yi, U. K.

doi:10.1016/j.cviu.2005.03.002

Cited by 57 publications

(24 citation statements)

References 10 publications

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“…In case 2 of Fig. 10(b), the error can be fixed by integrating the algorithm of lane recognition [31].…”

Section: γ δmentioning

confidence: 99%

A stereo matching algorithm based on top-view transformation and dynamic programming for road-vehicle detection

Lee¹,

Lee²,

Houshangi

2009

Int. J. Control Autom. Syst.

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This paper presents an algorithm to detect vehicles on roads and to measure inter-vehicle distance. Hypothesis generation and verification are the bases of the algorithm. The hypothesis generation is composed of 1) transformation of a pair of perspective stereo images into a pair of virtual stereo top-view images; 2) construction of polar accumulation functions (PAFs) for the stereo top-view images; and 3) stereo matching of PAFs by dynamic programming. The verification is comprised of 1) temporal matching of PAFs by dynamic programming and 2) verifying the potential positions of objects. Through multiple experiments in various environments, the proposed algorithm has been proven to be effective in detecting vehicles on roads and for measuring inter-vehicle distance.

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“…In case 2 of Fig. 10(b), the error can be fixed by integrating the algorithm of lane recognition [31].…”

Section: γ δmentioning

confidence: 99%

A stereo matching algorithm based on top-view transformation and dynamic programming for road-vehicle detection

Lee¹,

Lee²,

Houshangi

2009

Int. J. Control Autom. Syst.

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“…정지선과 횡단보도는 도로표면에 얇게 도포되어 있고, 다양한 잡음에 노출되어 있어 RF 기반 인식기술, 레이저 기반 인식기술로는 인식이 어려 운 것이 사실이다 [1]. 그리고 도심지역에서는 GPS/INS 기반 인식기술도 오차 발생가능성이 높다.…”

Section: 서 론unclassified

Design and Implementation of the Stop line and Crosswalk Recognition Algorithm for Autonomous UGV

Lee¹,

Yoon²

2014

Journal of Korean Institute of Intelligent Systems

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In spite of that stop line and crosswalk should be aware of the most basic objects in transportation system, its features extracted are very limited. In addition to image-based recognition technology, laser and RF, GPS/INS recognition technology, it is difficult to recognize. For this reason, the limited research in this area has been done. In this paper, the algorithm to recognize the stop line and crosswalk is designed and implemented using image-based recognition technology with the images input through a vision sensor. This algorithm consists of three functions.; One is to select the area, in advance, needed for feature extraction in order to speed up the data processing, 'Region of Interest', another is to process the images only that white color is detected more than a certain proportion in order to remove the unnecessary operation, 'Color Pattern Inspection', the other is 'Feature Extraction and Recognition', which is to extract the edge features and compare this to the previously-modeled one to identify the stop line and crosswalk. For this, especially by using case based feature comparison algorithm, it can identify either both stop line and crosswalk exist or just one exists. Also the proposed algorithm is to develop existing researches by comparing and analysing effect of in-vehicle camera installation and changes in recognition rate of distance estimation and various constraints such as backlight and shadow.

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“…In Figure 2 [19]. On the contrary, the straight line V = − + and the straight line V = − + where the parameter space in Figure 2(b) intersects at the same point and the collinear points in Figure 2(a) are correspondence [20]. According to this characteristic, given some specific points in Figure 2(a), the line equations connecting these points in Figure 2(b) can be calculated by Hough transform.…”

Section: Proposed Methodsmentioning

confidence: 99%

Lane Detection Based on Connection of Various Feature Extraction Methods

Jiang

2018

Advances in Multimedia

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Lane detection is a challenging problem. It has attracted the attention of the computer vision community for several decades. Essentially, lane detection is a multifeature detection problem that has become a real challenge for computer vision and machine learning techniques. Although many machine learning methods are used for lane detection, they are mainly used for classification rather than feature design. But modern machine learning methods can be used to identify the features that are rich in recognition and have achieved success in feature detection tests. However, these methods have not been fully implemented in the efficiency and accuracy of lane detection. In this paper, we propose a new method to solve it. We introduce a new method of preprocessing and ROI selection. The main goal is to use the HSV colour transformation to extract the white features and add preliminary edge feature detection in the preprocessing stage and then select ROI on the basis of the proposed preprocessing. This new preprocessing method is used to detect the lane. By using the standard KITTI road database to evaluate the proposed method, the results obtained are superior to the existing preprocessing and ROI selection techniques.

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A lane-departure identification based on LBPE, Hough transform, and linear regression

Cited by 57 publications

References 10 publications

A stereo matching algorithm based on top-view transformation and dynamic programming for road-vehicle detection

A stereo matching algorithm based on top-view transformation and dynamic programming for road-vehicle detection

Design and Implementation of the Stop line and Crosswalk Recognition Algorithm for Autonomous UGV

Lane Detection Based on Connection of Various Feature Extraction Methods

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