Motorcyclist's Helmet Wearing Detection Using Image Processing

Marayatr, Thepnimit; Kumhom, Pinit

doi:10.4028/www.scientific.net/amr.931-932.588

Cited by 21 publications

(12 citation statements)

References 11 publications

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“…Marayatr et al used the area feature to classify motorcycles and other vehicles [10]. This feature was classified using MLP.…”

Section: Motorcycle Detectionmentioning

confidence: 99%

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Improving Detection Performance of Helmetless Motorcyclists Using the Combination of HOG, HOP, and LDB Descriptors

2022

IJIES

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A significant number of motorcyclists that do not wear helmets lose their lives during a traffic accident, one of the major causes of death globally. This led to the design and development of a system capable of detecting helmetless motorcyclists in real-time to reduce the number of deaths. Generally, this system consists of 3 subsystems, namely moving object segmentation, motorcycle classification, and helmetless head detection. The Histograms of Oriented Gradients (HOG) descriptor has been used in preliminary studies, which resulted in fast computation time and high accuracy. However, this descriptor was less effective when applied to images with varying lighting and was unable to distinguish local pattern features. Therefore, this study proposed a new descriptor algorithm, namely Histogram of Oriented Phase and Gradient-Local Difference Binary (HOPG-LDB), which combined the HOG, Histogram of Oriented Phase (HOP), and Local Difference Binary (LDB) descriptors. The HOP was used to enhance the inability of the HOG to be effectively used in detecting images with varying lighting, and the LDB was used to extract local pattern features. The results showed that the proposed method can improve the accuracy of motorcycle classification and helmetless head detection compared to HOG, HOP, LDB, HOG-HOP, HOG-LDB, and HOP-LDB descriptors. Furthermore, the motorcycle classification accuracies of the proposed method were 97.05%, 97.25%, and 99.35% for the JSC1, JSC2, and database1 datasets. Meanwhile, the helmetless head detection results of the proposed method were 71.21%, 66.63%, and 91.73 for the JSC1, JSC2, and database2 datasets.

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“…Marayatr et al used the area feature to classify motorcycles and other vehicles [10]. This feature was classified using MLP.…”

Section: Motorcycle Detectionmentioning

confidence: 99%

“…Marayatr and Kumhon used the circular hough transform (CHT) descriptor for helmetless head detection. The result showed that it was found erroneous for the detection of 2 or more passengers [10]. Silva et al proposed the HOG descriptor, and the dataset for the study was taken in a static environment [4].…”

Section: Helmetless Head Detectionmentioning

confidence: 99%

Improving Detection Performance of Helmetless Motorcyclists Using the Combination of HOG, HOP, and LDB Descriptors

2022

IJIES

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“…The last step involves helmet detection. Colour based and circle Hough transform is used to detect helmet [7], [5], [10] and HOG descriptors can also be used for helmet detection [22]. Colour feature recognition is another option [15].…”

Section: Detection Of Non-helmet Riders and Extraction Of License Plamentioning

confidence: 99%

Detection of Non-Helmet Riders and Extraction of License Plate Number using Yolo v2 and OCR Method

J.*¹,

Tejas²,

Varshad³

et al. 2019

IJITEE

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In current situation, we come across various problems in traffic regulations in India which can be solved with different ideas. Riding motorcycle/mopeds without wearing helmet is a traffic violation which has resulted in increase in number of accidents and deaths in India. Existing system monitors the traffic violations primarily through CCTV recordings, where the traffic police have to look into the frame where the traffic violation is happening, zoom into the license plate in case rider is not wearing helmet. But this requires lot of manpower and time as the traffic violations frequently and the number of people using motorcycles is increasing day-by-day. What if there is a system, which would automatically look for traffic violation of not wearing helmet while riding motorcycle/moped and if so, would automatically extract the vehicles’ license plate number. Recent research have successfully done this work based on CNN, R-CNN, LBP, HoG, HaaR features,etc. But these works are limited with respect to efficiency, accuracy or the speed with which object detection and classification is done. In this research work, a Non-Helmet Rider detection system is built which attempts to satisfy the automation of detecting the traffic violation of not wearing helmet and extracting the vehicles’ license plate number. The main principle involved is Object Detection using Deep Learning at three levels. The objects detected are person, motorcycle/moped at first level using YOLOv2, helmet at second level using YOLOv3, License plate at the last level using YOLOv2. Then the license plate registration number is extracted using OCR (Optical Character Recognition). All these techniques are subjected to predefined conditions and constraints, especially the license plate number extraction part. Since, this work takes video as its input, the speed of execution is crucial. We have used above said methodologies to build a holistic system for both helmet detection and license plate number extraction.

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“…From these recordings, ships detection is performed, notably those exercising illegal fishing. As technologies develop nowadays, digital image processing techniques and artificial intelligence can be used for analysis of the object on digital image such as for motorcycle detection [2][3][4], renal tumor detection [5], traffic monitoring [6], virtual reality robot [7], motorcyclist's helmet wearing detection [8], blood typing [9], cotton pests detection [10], rice variety identification [11] and analysis of road damage [12]. Meanwhile, the studies on ship detection have been complied by percolating several methods, such as Support Vector Machine (SVM) [13][14], visual saliency and histogram [15], Discrete Wavelet Transform (DWT) [16], combination statistical methods, mathematical morphology and other signalprocessing techniques [17], and sea surface analysis [18].…”

Section: Introductionmentioning

confidence: 99%

Detection of Ship Using Image Processing and Neural Network

Sutikno¹,

Wibawa²,

Sasongko³

2018

TELKOMNIKA

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Indonesia is one of the countries in this world that has the most outstanding fishery potential. There are more than 3000 fish species under Indonesia's sea, yet the people are still not able to relish them completely. Illegal fishing by foreign ships in Indonesia's territorial sea is one of the reasons why this happens. In order to minimize this kind of loss, those ships should be detected automatically by implementing image processing and artificial intelligence techniques. The study proposed techniques for automatic detection of ships at sea on digital images. These techniques are global image thresholding and artificial neural network backpropagation. The result of this research is proposed of technique able to detect ship with 85% accuracy level. This method may be improved by adding more training data varieties.

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Motorcyclist's Helmet Wearing Detection Using Image Processing

Cited by 21 publications

References 11 publications

Improving Detection Performance of Helmetless Motorcyclists Using the Combination of HOG, HOP, and LDB Descriptors

Improving Detection Performance of Helmetless Motorcyclists Using the Combination of HOG, HOP, and LDB Descriptors

Detection of Non-Helmet Riders and Extraction of License Plate Number using Yolo v2 and OCR Method

Detection of Ship Using Image Processing and Neural Network

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