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@article{177125,
        author = {Hardhik Sai Palivela and Aaditya Gawali and Dr.R.Annie Uthra},
        title = {Real-Time Vehicle Detection and Traffic Density Estimation with Optimized Computational Efficiency},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {211-216},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177125},
        abstract = {There has been a dramatic change in the way traffic can be observed and controlled due to computer vision and machine learning in the recent past. Here, the focus would be on the creation and applicability of an intelligent traffic supervision system utilizing machine learning and computer vision. This system operates with the aim to provide real-time autonomous detection, classification and tracking of vehicles, identifying traffic rule infractions, and managing traffic congestion automatically. Fusing Artificial Intelligence such as Convolutional Neural Networks (CNN) aimed at recognizing objects and sharing videos helps the system increase the safety of the road while minimizing gridlock on the roads. In addition, it improves management practices by minimizing costs related to preparation of traffic reports, accidents, and infringements of road traffic order. This type of solution provides an economical, efficient and scalable option for monitoring traffic activities and has the likely impact to improve inner city traffic monitoring.},
        keywords = {Traffic Supervision, Machine learning, YOLOv8, Computer vision},
        month = {April},
        }