Robust detection system of illegal lane changes based on tracking of feature points

Lee, Hee-Sin; Jeong, Sunghwan; Lee, Joonwhoan

doi:10.1049/iet-its.2011.0210

Cited by 10 publications

(6 citation statements)

References 17 publications

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“…Previous studies of lane change violations at urban intersections have mainly focused on detection and discriminant methods [8–9]. However, to the best of our knowledge, few studies have been carried out to provide a thorough investigation of drivers’ motivation for this behavior.…”

Section: Introductionmentioning

confidence: 99%

What factors predict drivers’ self-reported lane change violation behavior at urban intersections? A study in China

Wang

Yan-ping

2019

PLoS ONE

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Lane change violations are a major cause of traffic conflicts and accidents at urban intersections and one of many road-safety issues in China. This study aims to explore the socio-psychological factors underlying drivers’ motivation for lane change violation behavior at urban intersections and examines how these factors predict this violation behavior. A self-reported questionnaire is designed by applying the construct of the theory of planned behavior (TPB) to collect data. Five hundred-six valid responses are received from the questionnaire survey conducted on the Internet in China. The data are then analyzed using structural equation modeling (SEM). The results of the analysis show that behavioral intention is the strongest predictor of self-reported lane change violation behavior at urban intersections. Perceived behavioral control has both direct and indirect effects on self-reported lane change violation behavior. Furthermore, attitude, subjective norms and perceived behavioral control are found to have significant correlations with drivers’ intention of lane change violations at urban intersections. The results of this study could provide a reference for designing more effective interventions to modify drivers’ lane change violation behavior at urban intersections.

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Section: Introductionmentioning

confidence: 99%

What factors predict drivers’ self-reported lane change violation behavior at urban intersections? A study in China

Wang

Yan-ping

2019

PLoS ONE

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“…Lee et al, [15] applied the concept of tracking feature points to perform real-time vehicle detection and lane change detection. Authors stated their approach as switch-independent which was not depending on the illumination conditions.…”

Section: Related Workmentioning

confidence: 99%

V-DaT: A Robust method for Vehicle Detection and Tracking

Anuj¹,

Gopalakrishna²,

Naveena³

et al. 2021

TURCOMAT

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Vision-based traffic surveillance has been one of the most promising fields for improvement and research. Still, many challenging problems remain unsolved, such as addressing vehicle occlusions and reducing false detection. In this work, a method for vehicle detection and tracking is proposed. The proposed model considers background subtraction concept for moving vehicle detection but unlike conventional approaches, here numerous algorithmic optimization approaches have been applied such as multi-directional filtering and fusion based background subtraction, thresholding, directional filtering and morphological operations for moving vehicle detection. In addition, blob analysis and adaptive bounding box is used for Detection and Tracking. The Performance of Proposed work is measured on Standard Dataset and results are encouraging.

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“…This can be accomplished through feature extraction, which plays a critical role in tracking [6, 17]. Feature point detection provides more stable detection of objects [18]. Common visual features utilised in these algorithms include colour, object boundaries, optical flow, and texture [17].…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluation of small unmanned aerial system highway volume and speed‐sensing applications

Ryan

Knodler

et al. 2020

IET Intelligent Trans Sys

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Small unmanned aerial systems (sUAS) have been utilised in the transportation industry in recent years to decrease the cost of projects and tasks while increasing safety. This is due to their ability to capture aerial images with reduced effort and time. Recently, these devices have begun to be used for traffic monitoring, given their ability to capture video above a roadway. Combined with object‐tracking techniques, vehicle data such as speeds, volumes, and trajectories could be extracted, providing an opportunity to revolutionize traffic data collection techniques. There exists a need to improve upon origin–destination volume and speed data collection procedures through the development of a low‐cost methodology to capture detailed data that would allow for more accurate analysis. This study evaluates a methodology and measures the accuracy of volume and speed data collected through sUAS aerial imagery using object tracking techniques. Using the developed methodology, vehicle volumes were tracked at 93% accuracy, and vehicle speeds were recorded with a 6.6% relative error. While future improvements could be made on this methodology as technology advances, this study reveals a low‐cost solution to collect vehicle data which could improve the efficiency of transportation studies, and in turn, improve safety.

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Robust detection system of illegal lane changes based on tracking of feature points

Cited by 10 publications

References 17 publications

What factors predict drivers’ self-reported lane change violation behavior at urban intersections? A study in China

What factors predict drivers’ self-reported lane change violation behavior at urban intersections? A study in China

V-DaT: A Robust method for Vehicle Detection and Tracking

Evaluation of small unmanned aerial system highway volume and speed‐sensing applications

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