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@article{195171,
        author = {Dr.V.Deepa and Mr.AghashKumar and Mr.S.Keerthivasan},
        title = {AI-Based Video Analysis and Automated Highlight Generation for Bike Ride Videos},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {7576-7579},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195171},
        abstract = {The growing popularity of bike ride and moto-vlog videos has increased the need for efficient video editing solutions. Long-duration ride footage often contains only a few engaging moments, making manual highlight extraction time-consuming. This project presents an AI-based video analysis system that automatically identifies and extracts important segments from bike ride videos. The proposed system combines computer vision techniques and audio signal processing to analyze scene transitions, motion intensity, and engine sound levels. A scoring mechanism is used to evaluate each segment, and high-scoring portions are selected to generate a summarized highlight video. The system reduces manual editing effort while preserving engaging content, making it useful for travel vloggers and digital content creators.},
        keywords = {Video Analysis, Highlight Detection, Motion Estimation, Scene Detection, Audio Processing, Artificial Intelligence},
        month = {March},
        }