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@article{202745,
        author = {Prof. Bharti Vivek Bandgar and Rahul Satyendra Tiwari and Atharva Wakchaure},
        title = {A Comprehensive Review of AI Driven Intelligent Surveillance System for Criminal Identification and Missing Person Detection with Privacy Preservation and Blockchain Integration},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {7930-7936},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202745},
        abstract = {The rapid expansion of urban surveillance infrastructure has increased the reliance on video monitoring systems for public safety, criminal identification, and missing person detection. However, traditional surveillance approaches largely depend on manual observation, resulting in limited scalability, delayed response, and reduced accuracy in complex environments. Recent advancements in artificial intelligence and computer vision have enabled automated face detection, recognition, and activity analysis, significantly improving detection performance. Existing research explores a wide range of techniques, including classical machine learning methods, deep learning-based facial recognition, real-time multi-camera systems, and image enhancement approaches. While these methods demonstrate improved accuracy under controlled conditions, several critical challenges remain unresolved. Current systems often struggle with cross-camera tracking, long-term identification of missing individuals, and robustness under occlusion, illumination variation, and appearance changes. Furthermore, most solutions lack mechanisms for privacy preservation, secure data sharing, tamper-proof evidence management, and transparent auditing. Emerging approaches such as federated learning and blockchain have been introduced to address some of these concerns; however, their integration with intelligent surveillance pipelines remains limited. This paper presents a comprehensive review of existing AI-driven surveillance systems, analyzing their methodologies, strengths, and limitations across multiple dimensions, including accuracy, scalability, privacy, and trust. The study identifies key research gaps and highlights the need for integrated, multi-modal, and privacy-preserving surveillance frameworks. The findings emphasize that future intelligent surveillance systems must combine advanced artificial intelligence techniques with secure and transparent data management to support reliable and legally accountable public safety applications.},
        keywords = {Artificial Intelligence, Intelligent Surveillance, Multi-Camera Tracking, Face Recognition, Blockchain-Based Security},
        month = {May},
        }