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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    Title :

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


    Additional title:

    Learning and Analytics in Intelligent Systems


    Contributors:

    Conference:

    International Conference on Data Science and Big Data Analysis ; 2024 ; Indore, India July 12, 2024 - July 13, 2024



    Publication date :

    2025-05-16


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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