Night-time Vehicle Detection for Automatic Headlight Beam Control

Sevekar, Pushkar; Dhonde, S. B.

doi:10.5120/ijca2017912737

Cited by 7 publications

(3 citation statements)

References 14 publications

(27 reference statements)

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“…They claimed that their proposed method's performance was effective, but some limitations were found in their work. A system based on image processing was developed by Pushkar, S. et al [23]. A nighttime vehicle detection system was developed by Bogacki, P. et al [15].…”

Section: Related Workmentioning

confidence: 99%

Vision-based On-Road Nighttime Vehicle Detection and Tracking Using Taillight and Headlight Features

Parvin¹,

Rozario²,

Islam³

2021

JCC

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An important and challenging aspect of developing an intelligent transportation system is the identification of nighttime vehicles. Most accidents occur at night owing to the absence of night lighting conditions. Vehicle detection has become a vital subject for research to ensure safety and avoid accidents. New vision-based on-road nighttime vehicle detection and tracking system are suggested in this survey paper using taillight and headlight features. Using computer vision and some image processing techniques, the proposed system can identify vehicles based on taillight and headlight features. For vehicle tracking, a centroid tracking algorithm has been used. Euclidean Distance method has been used for measuring the distances between two neighboring objects and tracks the nearest neighbor. In the proposed system two flexible fixed Region of Interest (ROI) have been used, one is the Headlight ROI, and another is the Taillight ROI that could adapt to different resolutions of the images and videos. The achievement of this research work is that the proposed two ROIs can work simultaneously in a frame to identify oncoming and preceding vehicles at night. The segmentation techniques and double thresholding method have been used to extract the red and white components from the scene to identify the vehicle headlights and taillights. To evaluate the capability of the proposed process, two types of datasets have been used. Experimental findings indicate that the performance of the proposed technique is reliable and effective in distinct nighttime environments for detection and tracking of vehicles. The proposed method has been able to detect and track double lights as well as single light such as motorcycle light and achieved average accuracy and average processing time of vehicle detection about 97.22% and 0.01 s per frame respectively.

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Section: Related Workmentioning

confidence: 99%

Vision-based On-Road Nighttime Vehicle Detection and Tracking Using Taillight and Headlight Features

Parvin¹,

Rozario²,

Islam³

2021

JCC

View full text Add to dashboard Cite

show abstract

“…For each pixel (x, y), all lines y = kx + b which may pass through it are mapped to k-b space (Hough space), and it is mapped to the ρ − θ space in the polar coordinate system. Then, we use a straight line expression with polar coordinates to represent the line in the image, as shown in (3).…”

Section: System Design Principlementioning

confidence: 99%

“…Hence, traffic accidents are likely to occur in the short time. So, automated auxiliary system can be used to remind drivers, and the driving at night is more safety [3]. In the field of automotive intelligent vehicles meeting system, such as ultrasonic radar, millimeter wave radar, laser radar and other detection equipment.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Lighting Recognition System Based on Erosion Algorithm and Effective Area Separation in 5G Vehicular Communication Networks

Chen

Bao

et al. 2019

IEEE Access

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For the safety of meeting vehicles at night, a design scheme of vehicle lighting recognition system for 5G vehicular communication networks is proposed in this paper. Firstly, a camera is used for image acquisition, and then the acquired RGB image is converted to a hue-saturation-lightness (HSL) image in Dedicated cloud. Secondly, a 3×3 convolution kemel is constructed, and the threshold of this HSL is used to create a mask. In which we traverse the original image data and perform uitwise operations on the original image, as well as mask by using the Urightest point as the center of calculation. Lastly, the effective area of the image is divided according to the farthest point of the lane line extension, which can shield the sky and street lights. In addition, all of interference points are excluded according to the symmetry of the headlights shape, the number of false detection is reduced effectively. So, the highlight areas can be marked to indicate the lights from the front vehicles. The results of the analysis are issued to the driving assistant system. The experimental results show that the system can identify the front vehicle lighting with an accuracy of 96.8%. With the help of the identified lighting position, the matrix lamp is set to realize the dynamic management of the beam, and it can be applied to traffic at night for preventing traffic accidents.

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A Binary-Directional Shape Descriptor for the Enhancement of Real-Time Automatic Headlight Control Function

Bogacki,

Długosz

2023

IEEE Access

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Night-time Vehicle Detection for Automatic Headlight Beam Control

Cited by 7 publications

References 14 publications

Vision-based On-Road Nighttime Vehicle Detection and Tracking Using Taillight and Headlight Features

Vision-based On-Road Nighttime Vehicle Detection and Tracking Using Taillight and Headlight Features

Vehicle Lighting Recognition System Based on Erosion Algorithm and Effective Area Separation in 5G Vehicular Communication Networks

A Binary-Directional Shape Descriptor for the Enhancement of Real-Time Automatic Headlight Control Function

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