Robust Lane Detection and Tracking for Real-Time Applications

Lee, Chanho; Moon, Ji‐Hyun

doi:10.1109/tits.2018.2791572

Cited by 139 publications

(84 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Lee and Moon [11] proposed a real-time lane detection algorithm with a Region of Interest (ROI) that is able to work with the high noise level and response in a shorter time. The system used the Kalman filter and a least square approximation of linear movement for lane tracking operation.…”

Section: Related Workmentioning

confidence: 99%

A Computer Vision based Lane Detection Approach

Haque¹,

Islam²,

Alam³

et al. 2019

IJIGSP

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

A Computer Vision based Lane Detection Approach

Haque¹,

Islam²,

Alam³

et al. 2019

IJIGSP

View full text Add to dashboard Cite

show abstract

“…In [17], an implementation of semantic image segmentation to enhance LiDAR-based road and lane detection was presented. In [18], using a proposed region of interest, the authors managed to reduce the calculation and high noise level for lane detection. Images are typically segmented into road, obstacles, sky, etc.…”

Section: Introductionmentioning

confidence: 99%

Managing Localization Uncertainty to Handle Semantic Lane Information from Geo-Referenced Maps in Evidential Occupancy Grids

Cherfaoui

Yang

2020

Sensors

View full text Add to dashboard Cite

Occupancy grid is a popular environment model that is widely applied for autonomous navigation of mobile robots. This model encodes obstacle information into the grid cells as a reference of the space state. However, when navigating on roads, the planning module of an autonomous vehicle needs to have semantic understanding of the scene, especially concerning the accessibility of the driving space. This paper presents a grid-based evidential approach for modeling semantic road space by taking advantage of a prior map that contains lane-level information. Road rules are encoded in the grid for semantic understanding. Our approach focuses on dealing with the localization uncertainty, which is a key issue, while parsing information from the prior map. Readings from an exteroceptive sensor are as well integrated in the grid to provide real-time obstacle information. All the information is managed in an evidential framework based on Dempster–Shafer theory. Real road results are reported with qualitative evaluation and quantitative analysis of the constructed grids to show the performance and the behavior of the method for real-time application.

show abstract

“…Moving object detection [1][2][3] is an image processing process used to extract moving objects in a sequence of images, usually based on image features such as edges, colors, and textures. For real-time intelligent surveillance [4,5], automated vehicle detection and tracking, personnel tracking, and many other applications, it is undoubtedly an indispensable area of research, not only in 2D motion observed but also in 3D scenes [6]. Globally, the objective of multi-target detection is to jointly estimate, at each observation time, the number of targets and their trajectories from noisy sensor measurements.…”

Section: Introductionmentioning

confidence: 99%

“…https ://motch allen ge.net/vis/PETS0 9-S2L1 5. http://www.robot s.ox.ac.uk/Activ eVisi on/index .html.…”

mentioning

confidence: 99%

A Systematic Algorithm for Moving Object Detection with Application in Real-Time Surveillance

Cui

Créput

2020

SN COMPUT. SCI.

View full text Add to dashboard Cite

Moving object detection and tracking from video sequences are a relevant research field since it can be used in many applications. While detection allows to return object shapes discovered in the image, tracking aims to individually identify and estimate individual trajectories of detected objects over time. Hence, detection can have a crucial impact on the overall tracking process. This paper focuses on detection. Currently, one of the leading detection algorithms includes frame difference method (FD), background subtraction method (BS), and optical flow method. Here, we present a detection algorithm based on the first two approaches since it is very adequate for fast real-time treatments, whereas optical flow has higher computation cost due to a dense estimation. A combination of FD and BS with Laplace filters and edge detectors is a way to achieve sparse detection fast. Thus, a main proposed contribution is the achievement of a systematic detection algorithm for moving target detection with a more elaborated combination of basic procedures used in real-time surveillance. Experimental results show that the proposed method has higher detection accuracy and better noise suppression than the current methods for standard benchmark datasets.

show abstract

Robust Lane Detection and Tracking for Real-Time Applications

Cited by 139 publications

References 13 publications

A Computer Vision based Lane Detection Approach

A Computer Vision based Lane Detection Approach

Managing Localization Uncertainty to Handle Semantic Lane Information from Geo-Referenced Maps in Evidential Occupancy Grids

A Systematic Algorithm for Moving Object Detection with Application in Real-Time Surveillance

Contact Info

Product

Resources

About