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@article{191576,
        author = {shiva},
        title = {A Deep Learning Framework for Person Re-Identification in Railway Surveillance Systems},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {7122-7127},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191576},
        abstract = {The growing concern for public safety in densely populated transportation hubs such as Indian Railways highlights the need for advanced surveillance systems. This project focuses on developing a deep learning-based Person Re-Identification model that can identify, track individuals across multiple surveillance cameras. Traditional surveillance methods depend on handcrafted visual features, which often lack precision and robustness in real-world scenarios involving varying poses, illumination, and camera angles. These limitations hinder effective monitoring and timely detection of suspicious activities. To overcome these challenges, the proposed system utilizes Convolutional Neural Networks to extract rich facial and body features, enabling accurate and consistent identification of individuals across different views. This intelligent Re-ID system enhances security monitoring efficiency and can significantly aid in maintaining safety across public transportation environments.},
        keywords = {Deep learning, Convolutional Neural Networks, Support vector machine, Random Forest, Advanced surveillance systems.},
        month = {January},
        }