scholarly journals A NEW APPROACH TO HIGHWAY LANE DETECTION BY USING HOUGH TRANSFORM TECHNIQUE

2017 ◽  
Author(s):  
Nur Shazwani Aminuddin ◽  
Masrullizam Mat Ibrahim ◽  
Nursabillilah Mohd Ali ◽  
Syafeeza Ahmad Radzi ◽  
Wira Hidayat Mohd Saad ◽  
...  
Keyword(s):  
Hough Transform ◽  
Average Rate ◽  
Lane Detection ◽  
Frame Rate ◽  
Video Frame ◽  
Night Time ◽  
The Road ◽  
Main Technique ◽  
On The Road ◽  
Width Measurements

This paper presents the development of a road lane detection algorithm using image processing techniques. This algorithm is developed based on dynamic videos, which are recorded using on-board cameras installed in vehicles for Malaysian highway conditions. The recorded videos are dynamic scenes of the background and the foreground, in which the detection of the objects, presence on the road area such as vehicles and road signs are more challenging caused by interference from background elements such as buildings, trees, road dividers and other related elements or objects. Thus, this algorithm aims to detect the road lanes for three significant parameter operations; vanishing point detection, road width measurements, and Region of Interest (ROI) of the road area, for detection purposes. The techniques used in the algorithm are image enhancement and edges extraction by Sobel filter, and the main technique for lane detection is a Hough Transform. The performance of the algorithm is tested and validated by using three videos of highway scenes in Malaysia with normal weather conditions, raining and a night-time scene, and an additional scene of a sunny rural road area. The video frame rate is 30fps with dimensions of 720p (1280x720) HD pixels. In the final achievement analysis, the test result shows a true positive rate, a TP lane detection  average rate of 0.925 and the capability to be used in the final application implementation.  

FAST LANE DETECTION USING DIRECTION KERNEL FUNCTION

2012 ◽  
Vol 10 (02) ◽  
pp. 1250017 ◽  
Author(s):  
YIMING NIE ◽  
BIN DAI ◽  
XIANGJING AN ◽  
ZHENPING SUN ◽  
TAO WU ◽  
...  
Keyword(s):  
Kernel Function ◽  
Lane Detection ◽  
Vanishing Point ◽  
Robust Detection ◽  
The Road ◽  
Road Lighting ◽  
Lane Tracking ◽  
Lane Marking ◽  
On The Road ◽  
Fast Lane

The lane information is essential to the highway intelligent vehicle applications. The direct description of the lanes is lane markings. Many vision methods have been proposed for lane markings detection. But in practice there are some problems to be solved by previous lane tracking systems such as shadows on the road, lighting changes, characters on the road and discontinuous changes in road types. Direction kernel function is proposed for robust detection of the lanes. This method focuses on selecting points on the markings edge by classification. During the classifying, the vanishing point is selected and the parts of the lane marking could form the lanes. The algorithm presented in this paper is proved to be both robust and fast by a large amount of experiments in variable occasions, besides, the algorithm can extract the lanes even in some parts of lane markings missing occasions.

scholarly journals Real-Time, Deep Learning Based Wrong Direction Detection

2020 ◽  
Vol 10 (7) ◽  
pp. 2453 ◽  
Author(s):  
Saidasul Usmankhujaev ◽  
Shokhrukh Baydadaev ◽  
Kwon Jang Woo
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Intelligent Transportation System ◽  
Warning Signal ◽  
Video Frame ◽  
Linear Quadratic ◽  
The Road ◽  
Direction Detection ◽  
On The Road ◽  
Deep Learning Model

In this paper, we develop a real-time intelligent transportation system (ITS) to detect vehicles traveling the wrong way on the road. The concept of this wrong-way system is to detect such vehicles as soon as they enter an area covered by a single closed-circuit television (CCTV) camera. After detection, the program alerts the monitoring center and triggers a warning signal to the drivers. The developed system is based on video imaging and covers three aspects: detection, tracking, and validation. To locate a car in a video frame, we use a deep learning method known as you only look once version 3 (YOLOv3). Therefore, we use a custom dataset for training to create a deep learning model. After estimating a car’s position, we implement linear quadratic estimation (also known as Kalman filtering) to track the detected vehicle during a certain period. Lastly, we apply an “entry-exit” algorithm to identify the car’s trajectory, achieving 91.98% accuracy in wrong-way driver detection.

The Multi-Scale Hough Transform Lane Detection Method Based on the Algorithm of Otsu and Canny

2014 ◽  
Vol 1042 ◽  
pp. 126-130 ◽  
Author(s):  
Yu Chai ◽  
Su Jing Wei ◽  
Xin Chun Li
Keyword(s):  
Hough Transform ◽  
Detection Method ◽  
Lane Detection ◽  
Transform Method ◽  
Canny Operator ◽  
The Road ◽  
Multi Scale ◽  
Lane Departure ◽  
Lane Line

In order to improve the accuracy of detecting lane for automatic vehicle driving, a method for detecting the straight part of Lane is proposed, which is the Multi-Scale Hough transform method for lane detection based on the algorithm of Otsu and Canny. First of all, by the methods of Otsu to segment image and use the morphology operation of erode and dilate to wipe off the information of roadside trees and fences to strengthen the road boundary characteristics.Then the lane edge and feature is gained by the canny operator. At last, using Standard Hough Transform, Progressiveness Probabilities Hough Transform and Multi-Scale Hough Transform complete the detection of lane’s straight part. The experimental results show that, Multi-Scale Hough Transform method can accurately detect the lane line and provide the reliable basis for the path planning, automatic follow-up vehicle driving and lane departure warning.

scholarly journals Lane Detection and Lane Change Warning as Advanced Driver Assistance Systems using Computer Vision

2019 ◽  
Vol 9 (2) ◽  
pp. 5540-5543 ◽  
Keyword(s):  
Road Network ◽  
Cost Effective ◽  
Lane Detection ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
The Road ◽  
Growing Economy ◽  
On The Road ◽  
New Processing ◽  
Processing Techniques

Road ways are the life line of any economy, for a country like India where economy isgrowing rapidly it is putting its toll on every sector for meeting the needs of the growing economy. Good’s and personal transport are becoming vital with time and money aspects and the roads and vehicles on the roads are expected to perform optimally drastically increasing the speed on the road network and constantly increasing and modifying the infrastructure needed to meet the demands. As the speed of the vehicle increases the accident rate and the damage caused by the collision will also increase. Safety of the road network is not to be compromised and proper systems to ensure the safe passage of the vehicle and proper warning systems are to be implemented. This system should be viable in all the condition and should be cost-effective. In this paper we are implementing a vision based system to identify the lane and other vehicles from the video it captures from a properly calibrated camera mounted on the front side of the vehicle. The system is designed to automatically and continuously detect the lines exploiting the new processing techniques and warning the driver if any other is in the breaking distance of the vehicle or if the vehicle is moving out of the lane. Cost effectiveness of the system is a major aspect as many of the available systems use equipment which very good at performing their task but are not affordable. Effort is put in making the system cost effective and not compromising with the reaction time and accuracy..

scholarly journals In-Season Nutrition Strategies and Recovery Modalities to Enhance Recovery for Basketball Players: A Narrative Review

2021 ◽  
Author(s):  
Jon K. Davis ◽  
Sara Y. Oikawa ◽  
Shona Halson ◽  
Jessica Stephens ◽  
Shane O’Riordan ◽  
...  
Keyword(s):  
Future Research ◽  
Time Variability ◽  
Night Time ◽  
Basketball Players ◽  
Compression Garments ◽  
Practical Applications ◽  
Wake Time ◽  
The Road ◽  
Multiple Challenges ◽  
On The Road

AbstractBasketball players face multiple challenges to in-season recovery. The purpose of this article is to review the literature on recovery modalities and nutritional strategies for basketball players and practical applications that can be incorporated throughout the season at various levels of competition. Sleep, protein, carbohydrate, and fluids should be the foundational components emphasized throughout the season for home and away games to promote recovery. Travel, whether by air or bus, poses nutritional and sleep challenges, therefore teams should be strategic about packing snacks and fluid options while on the road. Practitioners should also plan for meals at hotels and during air travel for their players. Basketball players should aim for a minimum of 8 h of sleep per night and be encouraged to get extra sleep during congested schedules since back-to back games, high workloads, and travel may negatively influence night-time sleep. Regular sleep monitoring, education, and feedback may aid in optimizing sleep in basketball players. In addition, incorporating consistent training times may be beneficial to reduce bed and wake time variability. Hydrotherapy, compression garments, and massage may also provide an effective recovery modality to incorporate post-competition. Future research, however, is warranted to understand the influence these modalities have on enhancing recovery in basketball players. Overall, a strategic well-rounded approach, encompassing both nutrition and recovery modality strategies, should be carefully considered and implemented with teams to support basketball players’ recovery for training and competition throughout the season.

The Influence of Driver Expectation when Recognizing Lighted Targets at Nighttime

2016 ◽  
Vol 60 (1) ◽  
pp. 489-493 ◽  
Author(s):  
Jeffrey W. Muttart ◽  
Swaroop Dinakar ◽  
Gregory Vandenberg ◽  
Michael Yosko
Keyword(s):  
Information Theory ◽  
Light Source ◽  
Search Model ◽  
Theory Approach ◽  
Night Time ◽  
The Road ◽  
Driving Scenario ◽  
The Difference ◽  
On The Road

Over the years, in a night time driving scenario, expectancy has been linked with faster night time recognition. This study tries to evaluate the ability of observers to identify illuminated objects on the road in the absence of an associative pattern. In this study 47 of 60 participants did not respond to a light source that was in the drivers’ travel lane ahead. Of those who did not respond to the light when directly ahead, 64% indicated that had seen it beforehand. When the light was 2 meters to the drivers’ right, 33% that saw the light failed to respond. All of the drivers who saw the light before striking it claimed that they thought it was off the road until too late. When the drivers did not know what the light source was, they could not decipher where the light was. However, once aware of the presence of the light the average recognition distance improved 192 meters (632 feet) with 100% recognition. These results fit well with the SEEV search model and an Information Theory approach to driver expectancy. Previous claims that the difference between expected and unexpected driver responses is a 2 to 1 ratio was not supported by this research.

scholarly journals Lane Detection Method with Impulse Radio Ultra-Wideband Radar and Metal Lane Reflectors

Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 324 ◽  
Author(s):  
Dae-Hyun Kim
Keyword(s):  
Detection Method ◽  
Impulse Radio ◽  
Lane Detection ◽  
Ultra Wideband ◽  
Vehicle Speed ◽  
Range Resolution ◽  
The Road ◽  
Detection Technology ◽  
On The Road ◽  
Wideband Radar

An advanced driver-assistance system (ADAS), based on lane detection technology, detects dangerous situations through various sensors and either warns the driver or takes over direct control of the vehicle. At present, cameras are commonly used for lane detection; however, their performance varies widely depending on the lighting conditions. Consequently, many studies have focused on using radar for lane detection. However, when using radar, it is difficult to distinguish between the plain road surface and painted lane markers, necessitating the use of radar reflectors for guidance. Previous studies have used long-range radars which may receive interference signals from various objects, including other vehicles, pedestrians, and buildings, thereby hampering lane detection. Therefore, we propose a lane detection method that uses an impulse radio ultra-wideband radar with high-range resolution and metal lane markers installed at regular intervals on the road. Lane detection and departure is realized upon using the periodically reflected signals as well as vehicle speed data as inputs. For verification, a field test was conducted by attaching radar to a vehicle and installing metal lane markers on the road. Experimental scenarios were established by varying the position and movement of the vehicle, and it was demonstrated that the proposed method enables lane detection based on the data measured.

scholarly journals Frequency and causes of kangaroo - vehicle collisions on an Australian outback highway

2006 ◽  
Vol 33 (1) ◽  
pp. 5 ◽  
Author(s):  
Ulrike Klöcker ◽  
David B. Croft ◽  
Daniel Ramp
Keyword(s):  
Temporal Distribution ◽  
Traffic Volume ◽  
Source Population ◽  
Road Mortality ◽  
Night Time ◽  
Vehicle Collisions ◽  
Road Kill ◽  
The Road ◽  
On The Road ◽  
The Impact

Kangaroo–vehicle collisions are frequent on Australian highways. Despite high economic costs, detrimental effects on animal welfare, and potential impacts on population viability, little research has been done to investigate the impact of road mortality on kangaroo populations, where and why accidents occur, and how the collisions can be mitigated. We therefore collected data on species (Macropus rufus, M. giganteus, M. fuliginosus, M. robustus), sex and age of kangaroos killed on a 21.2-km bitumenised section of outback highway over 6 months in far western New South Wales, Australia. The spatial and temporal distribution of road-killed kangaroos was investigated in relation to the cover and quality of road-side vegetation, road characteristics, the density of kangaroos along the road, climatic variables and traffic volume. A total of 125 kangaroos were found killed on the road at a rate of 0.03 deaths km–1 day–1. Grey kangaroos of two species (M. giganteus, M. fuliginosus) were under-represented in the road-kill sample in comparison with their proportion in the source population estimated during the day. No bias towards either sex was found. The age structure of road-killed kangaroos was similar to age structures typical of source kangaroo populations. Road-kills mainly occurred in open plains country. In road sections with curves or stock races, road-kill frequencies were higher than expected. Greater cover and greenness of roadside vegetation at the verge probably attracted kangaroos to the road and variation in this vegetation affected the spatial distribution of road-kills. The temporal distribution of road-kills was positively correlated with the volume of night-time traffic. The probability of a kangaroo–vehicle collision increased exponentially with traffic volume. Results are discussed in relation to the potential for mitigation of kangaroo–vehicle collisions.

Research on the Lane Detection Algorithm Based on Zoning Hough Transformation

2012 ◽  
Vol 490-495 ◽  
pp. 1862-1866 ◽  
Author(s):  
Chao Fan ◽  
Li Long Hou ◽  
Shuai Di ◽  
Jing Bo Xu
Keyword(s):  
Hough Transform ◽  
Ambient Noise ◽  
Detection Method ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Dominant Position ◽  
Hough Transformation ◽  
The Road ◽  
Lane Marking ◽  
Sobel Operators

In order to improve the adaptability of the lane detection algorithm under complex conditions such as damaged lane lines, covered shadow, insufficient light, the rainy day etc. Lane detection algorithm based on Zoning Hough Transform is proposed in this paper. The road images are processed by the improved ±45° Sobel operators and the two-dimension Otsu algorithm. To eliminate the interference of ambient noise, highlight the dominant position of the lane, the Zoning Hough Transform is used, which can obtain the parameters and identify the lane accurately. The experiment results show the lane detection method can extract the lane marking parameters accurately even for which are badly broken, and covered by shadow or rainwater completely, and the algorithm has good robustness.

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