Robust hypothesis generation method using binary blob analysis for multi‐lane detection

Piao, Jingchun; Shin, Hyunchul

doi:10.1049/iet-ipr.2016.0506

Cited by 34 publications

(20 citation statements)

References 22 publications

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“…Niu et al [2], [28] used a modified Hough transform to extract lane profile segments and used DBSCAN (Density-Based Spatial Clustering of applications with noise) clustering algorithm. Li et al [29] introduced a new method of pre-processing and ROI selection using the HSV (hue, saturation, value) color transformation to extract the white features and add preliminary edge feature detection in the pre-processing stage and then select ROI on the basis of the proposed pre-processing.…”

Section: Related Workmentioning

confidence: 99%

“…by employing various technologies such as Global Positioning System (GPS), Light Detection and Ranging (LiDAR), as well as optical sensors, i.e., cameras [2]. The main advantage of employing GPS or LiDAR is that regardless of the weather condition the data is directly acquired.…”

Section: Introductionmentioning

confidence: 99%

“…Although, LiDAR is popularly used due to its high resolution images, and highly reliable sensor, the cost is very high. The most typical passive sensor is an optical sensor, like a camera [2]. A standard vision-based Lane Departure Warning System (LDWS) is mainly composed of three parts: Lane detection, lane tracking, and lane departure warning [3].…”

Section: Introductionmentioning

confidence: 99%

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A Lane Tracking Method Based on Progressive Probabilistic Hough Transform

et al. 2020

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Lane departure warning systems have gained considerable research interest in the past decade for its promising usage in automotive, where lane detection and tracking is applied. However, it is a challenging task to improve the robustness of lane detection due to environmental factors, such as perspective effect, possible low visibility of lanes, and partial occlusions. To deal with these issues, we propose a reliable vision-based real-time lane markings detection and tracking system that can adapt to various environmental conditions. The lane detection is composed of three stages: pre-processing, Adaptive Region of Interest (AROI) setting, and lane marking detection and tracking. In the pre-processing stage, smoothing and edge detection operators are applied on input frames to automatically obtain binary images, then, lane markings segmentation are carried out. After that, An Adaptive Region of Interest is extracted to reduce the computational complexity. In the subsequent detection stages, Kalman filter is employed to track road boundaries detected in the AROI using Progressive Probabilistic Hough Transform (PPHT) in the next frame. Based on road boundaries and the vehicle's position, the proposed algorithm decides if the vehicle has drifted off the lane. For the performance evaluation of lane detection and tracking, real-life datasets for both urban roads and highways in various lighting conditions are used. Applying our method to Catltech dataset, the average correct detection rate is 93.82%. In addition, the proposed method outperforms that of the state-of-the-art methods in processing time (21.54ms/frame). INDEX TERMS Advanced driving assistance systems (ADAS), lane detection and tracking, lane departure warning system (LDWS), progressive probabilistic Hough transform (PPHT), adaptive region of interest (AROI).

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Lane Tracking Method Based on Progressive Probabilistic Hough Transform

et al. 2020

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“…Moreover, the estimation of PVP affected by the outliers at the operation of Least Squares Fitting and sometimes unsuccessful detection of lane happens due to the plus-minus peaks value selection. Piao et al [16] developed a lane marking technique based on the binary blob analysis. To eliminate the perspective consequence from the road surfaces, the system used vanishing point detection and inverse perspective mapping.…”

Section: Related Workmentioning

confidence: 99%

A Computer Vision based Lane Detection Approach

Haque¹,

Islam²,

Alam³

et al. 2019

IJIGSP

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Automatic lane detection to help the driver is an issue considered for the advancement of Advanced Driver Assistance Systems (ADAS) and a high level of application frameworks because of its importance in drivers and passerby safety in vehicular streets. But still, now it is a most challenging problem because of some factors that are faced by lane detection systems like as vagueness of lane patterns, perspective consequence, low visibility of the lane lines, shadows, incomplete occlusions, brightness and light reflection. The proposed system detects the lane boundary lines using computer vision-based technologies. In this paper, we introduced a system that can efficiently identify the lane lines on the smooth road surface. Gradient and HLS thresholding are the central part to detect the lane lines. We have applied the Gradient and HLS thresholding to identify the lane line in binary images. The color lane is estimated by a sliding window search technique that visualizes the lanes. The performance of the proposed system is evaluated on the KITTI road dataset. The experimental results show that our proposed method detects the lane on the road surface accurately in several brightness conditions.

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“…With regard to line detection, we usually have two methods which include feather-based method and modelbased methods. Niu et al used a modified Hough transform to extract segments of the lane profile and used DBSCAN (density based spatial application noise clustering) clustering algorithm for clustering [14]. In 2016, Mammeri et al used progressive probabilistic Hough transform combined with maximum stable extreme area (MSER) technology to identify and detect lane lines and utilized Kalman filter to achieve continuous tracking [15].…”

Section: Introductionmentioning

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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Robust hypothesis generation method using binary blob analysis for multi‐lane detection

Cited by 34 publications

References 22 publications

A Lane Tracking Method Based on Progressive Probabilistic Hough Transform

A Lane Tracking Method Based on Progressive Probabilistic Hough Transform

A Computer Vision based Lane Detection Approach

Lane Detection Based on Connection of Various Feature Extraction Methods

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