Helmet violation processing using deep learning

Raj, Kamesh; Chairat, Aphinya; Timtong, Vasan; Dailey, Matthew N.; Ekpanyapong, Mongkol

doi:10.1109/iwait.2018.8369734

Cited by 29 publications

(10 citation statements)

References 8 publications

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“…Meanwhile, Vishnu et al used the AlexNet model on both light and heavy traffic [16]. Raj KC et al also used the AlexNet model and erroneous detections were made for riders putting on hats [19]. Forero used the iter_45, Inception-V3 network, and full ImageNet network models [20].…”

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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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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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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“…Finally, the recognition of motorcyclists without helmets is done by cropping the region of the motorcyclist head (which may be highly dependent on the perspective view) and fed to another CNN model which performs binary classification, according to the features trained from motorcyclist heads. Helmet detection is also investigated in [76]. The motorcycle detector uses a linear SVM for a feature vector classification based on histograms of oriented gradienta (HOG).…”

Section: Other Approachesmentioning

confidence: 99%

Detection of Motorcycles in Urban Traffic Using Video Analysis: A Review

Espinosa

Velastín

Branch

2021

IEEE Trans. Intell. Transport. Syst.

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Motorcycles are Vulnerable Road Users (VRU) and as such, in addition to bicycles and pedestrians, they are the traffic actors most affected by accidents in urban areas. Automatic video processing for urban surveillance cameras has the potential to effectively detect and track these road users. The present review focuses on algorithms used for detection and tracking of motorcycles, using the surveillance infrastructure provided by CCTV cameras. Given the importance of results achieved by Deep Learning theory in the field of computer vision, the use of such techniques for detection and tracking of motorcycles is also reviewed. The paper ends by describing the performance measures generally used, publicly available datasets (introducing the Urban Motorbike Dataset (UMD) with quantitative evaluation results for different detectors), discussing the challenges ahead and presenting a set of conclusions with proposed future work in this evolving area.

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“…Moreover, in July 2019, the same approach to detecting the absence of helmets on motorcyclists with or without helmets was presented by Yogameena B. et al [48]; it was different in that it combined a CNN with a Gaussian Mixture Model (GMM) [57]. Furthermore, in 2020, a system that uses image processing and CNN networks was developed by Raj K. C. et al [58] for the identification of the motorcyclists who violate helmet laws.…”

Section: Related Workmentioning

confidence: 99%

Smart Helmet 5.0 for Industrial Internet of Things Using Artificial Intelligence

Campero-Jurado

Márquez-Sánchez

Quintanar-Gómez

et al. 2020

Sensors

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Information and communication technologies (ICTs) have contributed to advances in Occupational Health and Safety, improving the security of workers. The use of Personal Protective Equipment (PPE) based on ICTs reduces the risk of accidents in the workplace, thanks to the capacity of the equipment to make decisions on the basis of environmental factors. Paradigms such as the Industrial Internet of Things (IIoT) and Artificial Intelligence (AI) make it possible to generate PPE models feasibly and create devices with more advanced characteristics such as monitoring, sensing the environment and risk detection between others. The working environment is monitored continuously by these models and they notify the employees and their supervisors of any anomalies and threats. This paper presents a smart helmet prototype that monitors the conditions in the workers’ environment and performs a near real-time evaluation of risks. The data collected by sensors is sent to an AI-driven platform for analysis. The training dataset consisted of 11,755 samples and 12 different scenarios. As part of this research, a comparative study of the state-of-the-art models of supervised learning is carried out. Moreover, the use of a Deep Convolutional Neural Network (ConvNet/CNN) is proposed for the detection of possible occupational risks. The data are processed to make them suitable for the CNN and the results are compared against a Static Neural Network (NN), Naive Bayes Classifier (NB) and Support Vector Machine (SVM), where the CNN had an accuracy of 92.05% in cross-validation.

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Helmet violation processing using deep learning

Cited by 29 publications

References 8 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 Motorcycles in Urban Traffic Using Video Analysis: A Review

Smart Helmet 5.0 for Industrial Internet of Things Using Artificial Intelligence

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