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@article{199423,
        author = {MOHAMMED SHAZEB and N HARIKA and YASWANTHI RAMA and ISHANTH SHETTY and RRS RAVIKUMAR},
        title = {SMART TENNIS ANALYSIS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {12276-12284},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=199423},
        abstract = {The advancement of computer vision and deep learning has enabled automated and data-driven sports analytics, particularly in tennis performance evaluation. Traditional analysis methods are manual, time-consuming, and subjective. This paper proposes an automated tennis analysis system that processes match videos to extract meaningful performance metrics. The system follows an end-to-end pipeline including frame extraction, object detection, court key point detection, object tracking, and performance analysis. Player detection is performed using YOLOv8, while tennis ball detection uses a finetuned YOLOv5 model. A convolutional neural network is used for court key point detection to map player and ball positions accurately. Tracking techniques are applied to analyze movement patterns and compute metrics such as player speed, ball speed, shot count, and distance covered. The system generates annotated videos and statistical outputs for performance evaluation. Experimental results show high accuracy in player detection and satisfactory ball detection, with minor limitations under challenging conditions. The proposed solution is cost-effective, scalable, and reduces manual effort, demonstrating the potential of AI in modern sports analytics. Keywords— Computer vision, Deep learning, Tennis analytics, YOLO, Performance analysis.},
        keywords = {},
        month = {April},
        }