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@article{169574,
        author = {Bhoomika Manjunath and Dr. Hemavathy R},
        title = {Enhancing Video Summarization with advanced Object Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {1873-1878},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169574},
        abstract = {In the era of digital proliferation, the abundance of video content across online platforms, surveillance systems, and personal archives has necessitated the development of efficient methods for summarizing and comprehending vast amounts of video data. Traditional video summarization approaches, such as keyframe extraction, temporal clustering, and scene analysis, often fall short in capturing crucial visual elements and events, resulting in less effective summaries. Recent advancements in object detection, driven by deep learning and Convolutional Neural Networks (CNNs), have significantly improved the accuracy of identifying and localizing objects within video frames. By incorporating object detection into the video summarization process, this paper proposes a novel approach that leverages these advancements to produce more concise, contextually relevant, and informative video summaries. The integration of object detection allows for the identification of key objects, actions, and interactions, thereby enhancing the comprehensiveness and relevance of the resulting summaries.},
        keywords = {},
        month = {November},
        }