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@article{177969,
        author = {Podili Muni Jyothieswar and Pilli Charitha and Chinthala Pavani and Erugu Yekalavya and Koduru GunaSekhar and Nemallapudi Sukumar},
        title = {A Survey On Advanced Surveillance System for Intelligent Threat Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {1774-1782},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177969},
        abstract = {Smart surveillance systems have undergone remarkable advancements with the integration of deep learning techniques, significantly enhancing their ability to monitor and secure environments. These systems use artificial intelligence to detect, track, and analyze human activities in real time, ensuring heightened security, improved efficiency, and automation. By leveraging deep learning models such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), these systems can accurately recognize patterns, detect anomalies, and respond to potential threats autonomously with minimal human intervention. The adoption of deep learning in surveillance has led to substantial improvements in real-time threat detection, facial recognition, and behavioral analysis. These systems are widely applied in public safety, traffic monitoring, and crime prevention. However, they also pose challenges related to computational costs, privacy concerns, and the necessity of high-quality video feeds. This report explores the architecture, implementation, benefits, and drawbacks of deep learning-based surveillance systems, emphasizing their transformative role in modern security infrastructure.},
        keywords = {Artificial Intelligence (AI), Deep Learning (DL), Image Processing (IP), TensorFlow, Single Shot Multi-Box Detector (SSD), Raspberry Pi.},
        month = {May},
        }