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<p>Uncontrolled road traffic conditions are commonly seen in South Asian countries, which result in the majority of motorcycle accidents due to triple riding, and helmetless driving traffic violation incidents. Triple riding is a dangerous act that can result in serious legal consequences. Each rider should be aware of the stringent traffic safety regulations of helmet wear and triple-riding violations. Public safety can be improved by reducing the number of road&#xa0;accidents. To do this, these riders must be identified and prosecuted. With little assistance from humans, the automated traffic monitoring system can enforce rigorous adherence to traffic laws. The current methods are effective when applied to widely used datasets, like Kaggle and COCO, which offer a helpful research platform. However, it is difficult to obtain satisfactory detection&#xa0;accuracies&#xa0;because this dataset contains minimal triple-riding images and lacks the sensation of realistic traffic CCTV images obtained from specific heights and angles. We provide a real-time solution that employs surveillance cameras placed at various angles and heights to detect two-wheelers, identify the number of riders, and recognize the vehicle involved in this traffic violation. To&#xa0;address challenging environments like occlusions and precise vehicle detection from a long distance we use the ResNet18-based DetectNet_v2 model.&#xa0;To reliably predict triple riding from several riders sitting on a two-wheeler and extract license plate information, we employ a cutting-edge YOLOv8 object-detection algorithm that operates on the Darknet framework. After experiment analysis, we found that&#xa0;our proposed model&#xa0;demonstrated a promising triple-rider,&#xa0;two-wheeler,&#xa0;and numberplate detection accuracy of 91.42%, 98%, and 81% respectively under challenging situations.</p>

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Automatic two-wheeler rider identification and triple-riding detection in surveillance systems using deep-learning models

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月15日

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  • Uttam U. Deshpande,
  • Supriya Shanbhag,
  • Rudragoud Patil,
  • Ramchandra Alias Ameet Chate,
  • Sufola Das Chagas Silva Araujo,
  • Kevin Pinto,
  • J. Arjun,
  • Mayankraj Patil,
  • Balagouda Nirwani

摘要

Uncontrolled road traffic conditions are commonly seen in South Asian countries, which result in the majority of motorcycle accidents due to triple riding, and helmetless driving traffic violation incidents. Triple riding is a dangerous act that can result in serious legal consequences. Each rider should be aware of the stringent traffic safety regulations of helmet wear and triple-riding violations. Public safety can be improved by reducing the number of road accidents. To do this, these riders must be identified and prosecuted. With little assistance from humans, the automated traffic monitoring system can enforce rigorous adherence to traffic laws. The current methods are effective when applied to widely used datasets, like Kaggle and COCO, which offer a helpful research platform. However, it is difficult to obtain satisfactory detection accuracies because this dataset contains minimal triple-riding images and lacks the sensation of realistic traffic CCTV images obtained from specific heights and angles. We provide a real-time solution that employs surveillance cameras placed at various angles and heights to detect two-wheelers, identify the number of riders, and recognize the vehicle involved in this traffic violation. To address challenging environments like occlusions and precise vehicle detection from a long distance we use the ResNet18-based DetectNet_v2 model. To reliably predict triple riding from several riders sitting on a two-wheeler and extract license plate information, we employ a cutting-edge YOLOv8 object-detection algorithm that operates on the Darknet framework. After experiment analysis, we found that our proposed model demonstrated a promising triple-rider, two-wheeler, and numberplate detection accuracy of 91.42%, 98%, and 81% respectively under challenging situations.