Traffic Safety Monitoring System

EasyChair Preprint 12772

Abstract

The integration of advanced technologies in traffic enforcement represents a paradigm shift in urban safety management. By harnessing the power of Convolutional Neural Networks (CNNs) and Easy OCR, this project pioneers a holistic approach to address the complex issue of traffic violations. The CNN’s ability to discern number plates, coupled with Easy OCR’s proficiency in extracting alphanumeric data, forms a synergistic alliance that promises unparalleled accuracy in offender identification. Furthermore, this research undertakes an in-depth analysis of the socio-economic impact of traffic violations. The resulting data illuminates the potential cost savings and lives preserved through the reduction of accidents and injuries. By curbing reckless driving behaviors, the project aligns with broader societal goals of reducing healthcare expenses and lost productivity due to traffic-related incidents. Moreover, the study takes into consideration the scalability and adaptability of this technology. With the rapid urbanization and motorization witnessed in India, a system that can efficiently adapt to varying traffic densities and infrastructural complexities is paramount. The robustness of the proposed solution ensures its applicability not only in bustling metropolitan areas but also in emerging urban centers, thereby democratizing access to advanced traffic safety measures. Additionally, this project underscores the significance of international collaboration in the realm of traffic safety. By sharing insights and methodologies, the global community can collectively work towards harmonizing traffic regulations and enforcement strategies. This collaborative approach not only fosters mutual learning but also bolsters the collective ability to tackle traffic safety challenges on a global scale. In summary, this research initiative stands at the forefront of leveraging technology for safer roadways.

Keyphrases: Directions, I. INTRODUCTION, II. LITERATURE REVIEW, III. METHODOLOGY, IV. CHALLENGES AND FUTURE, V. CONCLUSION AND FUTURE, VI. REFERENCE, work

@booklet{EasyChair:12772,
  author    = {Shivanagendar Mandala and Srinivas Reddy Masireddy and Kiran Banoth and Pradeep Chandra Mandru and Rasna Patel},
  title     = {Traffic Safety Monitoring System},
  howpublished = {EasyChair Preprint 12772},
  year      = {EasyChair, 2024}}