Machine Learning based Surveillance System for Detection of Bike Riders without Helmet and Triple Rides

Saumya, Apoorva; Gayathri, V. M.; Venkateswaran, K.; Kale, Sarthak; Sridhar, N

doi:10.1109/icosec49089.2020.9215266

Cited by 27 publications

(5 citation statements)

References 13 publications

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“…While there are promising prospects with the CNN model, particularly with additional training datasets, the Yolo-V3 model stands out in addressing crucial body positions such as the eyes, face, head, neck, and arms. The successes of Yolo-V3 models have been reported in real-time workplace monitoring, showcasing its capability to report multiple and significant positions (Saumya et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Toward the design of persuasive systems for a healthy workplace: a real-time posture detection

Ataguba,

Orji

2024

Front. Big Data

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Persuasive technologies, in connection with human factor engineering requirements for healthy workplaces, have played a significant role in ensuring a change in human behavior. Healthy workplaces suggest different best practices applicable to body posture, proximity to the computer system, movement, lighting conditions, computer system layout, and other significant psychological and cognitive aspects. Most importantly, body posture suggests how users should sit or stand in workplaces in line with best and healthy practices. In this study, we developed two study phases (pilot and main) using two deep learning models: convolutional neural networks (CNN) and Yolo-V3. To train the two models, we collected posture datasets from creative common license YouTube videos and Kaggle. We classified the dataset into comfortable and uncomfortable postures. Results show that our YOLO-V3 model outperformed CNN model with a mean average precision of 92%. Based on this finding, we recommend that YOLO-V3 model be integrated in the design of persuasive technologies for a healthy workplace. Additionally, we provide future implications for integrating proximity detection taking into consideration the ideal number of centimeters users should maintain in a healthy workplace.

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Section: Discussionmentioning

confidence: 99%

Toward the design of persuasive systems for a healthy workplace: a real-time posture detection

Ataguba,

Orji

2024

Front. Big Data

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“…This is suggested in [4] & [5] to be using YOLOv3 [6] method is used to detect the motorcyclists wearing helmet however, no motorbike recognition is verified. In [7] and [8], we initially detected the motorbike & man inside the image using the YOLOv3 method, and afterwards they evaluated the overlapped volume of the grid cell between both the motorbike and man to identify the individual riding the motorbike. The YOLOv3 algorithms was then used to determine whether motorcyclists were wearing a helmet.…”

Section: Related Work a Helmet Detectionmentioning

confidence: 99%

Detection of License Plate If Helmet Not Found

Minavathi¹

2022

IJRASET

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Motorbike accidents has been growing more in the current years, due to high usage of superbike which is nearly 300- 1200cc, which may reach the speed of 200-300 kmph, in that hyper speed, there are more chances of accidents. In India not only over speeding causes accidents, due to bad roads and road bumps will cause accident even though people riding in low speed. Also, if raider violates the traffic rules cops may try to catch the riders, it might be bit difficult to catch the riders sometime they may escape, it will be too difficult to board a case on them by capturing the License Plate number manually, therefore its highly necessary to build an automated based helmet and license plate recognition based on Deep Learning system using YOLO algorithm

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“…This motivated them to take the approach even further and implement a transfer learning approach to this problem. Apoorva Saumya [4] has discussed "Detection of Bike Riders without Helmet and Triple Rides" using the YOLOv3 Model which is a consistent type of YOLO model. These days video Surveillance based systems have turned into a significant gear to remain a track of any very crook or hostile to law movement in current human advancement.…”

Section: IImentioning

confidence: 99%

Helmet Detection on Motorcyclists Using Deep Learning

Sannidhi¹

2022

IJRASET

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The most popular vehicles in India are motorcycles and motorbikes. Particularly in India, traffic accidents are a leading cause of death worldwide. Although two-wheeled motorbikes are a logical option for a practical mode of transportation, they also greatly raise the incidence of fatalities and injuries in traffic accidents. Despite government regulations, many people still choose not to wear helmets when driving. A person may choose not to wear a helmet due to their own hasty or deliberate actions. In an effort to increase awareness in society, this project encourages the strict use of helmets and points individuals in the direction of safety. The program helps the traffic police find the violations while not slowing down traffic. When receiving a bill through SMS as they arrive at their homes, no one would ever attempt to break the law. The initiative revolves around the idea of transfer learning. YOLO is the object detection algorithm employed here (You Only Look Once). To try to identify people without helmets and send a snapshot of them to the closest police station, the project uses feature detection, which is the idea behind object detection. We intend to update the project so that it can recognize the license plate, even match the information of the person using the plate, and send an SMS bill to the offender.

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Machine Learning based Surveillance System for Detection of Bike Riders without Helmet and Triple Rides

Cited by 27 publications

References 13 publications

Toward the design of persuasive systems for a healthy workplace: a real-time posture detection

Toward the design of persuasive systems for a healthy workplace: a real-time posture detection

Detection of License Plate If Helmet Not Found

Helmet Detection on Motorcyclists Using Deep Learning

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