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Effective traffic management is essential for mitigating congestion, enhancing road safety, and reducing environmental impacts in urban areas, that is why vehicle detection and counting systems have long been a goal of computer scientists. This research explores advanced methodologies and technologies for traffic management, focusing on implementation using the Haar Cascade Classifier with preprocessing with machine learning techniques and the system is useful for realtime traffic analysis, incident detection, and adaptive signal control. This proposed approach can detect a vehicle with 94% accuracy using Haar cascades. The system significantly improves the increasing road traffic management strategies and reduces congestion. Future incarnations may be focused on improving the robustness of the proposed system against varying environmental conditions.

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Vehicle Detection Using Haar Features for Effective Traffic Management Using Machine Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Jyoti Kukade,
  • Megha Patidar,
  • Rahul S. Pawar,
  • Trapti Mishra,
  • Vidhya Barpha,
  • Prashant Panse

摘要

Effective traffic management is essential for mitigating congestion, enhancing road safety, and reducing environmental impacts in urban areas, that is why vehicle detection and counting systems have long been a goal of computer scientists. This research explores advanced methodologies and technologies for traffic management, focusing on implementation using the Haar Cascade Classifier with preprocessing with machine learning techniques and the system is useful for realtime traffic analysis, incident detection, and adaptive signal control. This proposed approach can detect a vehicle with 94% accuracy using Haar cascades. The system significantly improves the increasing road traffic management strategies and reduces congestion. Future incarnations may be focused on improving the robustness of the proposed system against varying environmental conditions.