FindMe: AI-based Surveillance System for Missing People
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| Natura: | Recurso digital |
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Zenodo
2026
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| _version_ | 1866901722758119424 |
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| author | Prof. Rashmi Tuptewar Vrushank Bhavsar Tanaya Patil Naman Nagota Lavanya Rathi |
| author_facet | Prof. Rashmi Tuptewar Vrushank Bhavsar Tanaya Patil Naman Nagota Lavanya Rathi |
| contents | In an increasingly interconnected world, the issue of missing persons has emerged as a critical public safety challenge, often constrained by delayed reporting, fragmented data sharing, and manual video analysis. Conventional surveillance review processes are labor-intensive, time-consuming, and prone to human oversight, which significantly reduces the chances of timely recovery. To address these limitations, this project titled "FindMe: AI-Powered Surveillance System for Missing Person Detection" proposes the design and development of an intelligent, real-time identification framework that integrates facial recognition, deep learning, and computer vision technologies. The proposed system introduces a unified architecture that connects surveillance networks, law enforcement databases, and communication channels into a single AI-driven ecosystem. Leveraging pre-trained Convolutional Neural Network (CNN) models such as VGGFace, FaceNet, and ResNet, the system performs automatic face detection, feature extraction, and similarity matching across live and recorded video feeds. To ensure real-time processing, technologies like OpenCV, TensorFlow, and scalable cloud-based APIs are utilized for efficient computation and streaming analytics. Once a match is detected, automated alerts are instantly generated and transmitted to law enforcement agencies through web dashboards, SMS, and email notifications. The project also emphasizes scalability, accuracy, and ethical considerations in surveillance analytics. Measures such as threshold optimization, false-positive reduction, and dynamic database updates enhance system reliability and minimize misidentification risks. Additionally, privacy-preserving techniques and encryption protocols are integrated to safeguard sensitive data. The framework is tested and validated using publicly available datasets such as LFW (Labeled Faces in the Wild) and VGGFace2, ensuring robustness across diverse demographics and environmental conditions. By transforming traditional surveillance into an intelligent and proactive monitoring network, FindMe aims to revolutionize missing person investigations through automation, rapid information retrieval, and enhanced situational awareness. This solution not only accelerates response times but also demonstrates the potential of AI in augmenting public safety and humanitarian efforts through technology-driven intelligence. This research-driven system aspires to bridge the gap between academic machine learning models and industry-grade fraud prevention mechanisms by enabling scalable, low-latency, and continuously adaptive fraud detection pipelines. Beyond its technical implementation, the project contributes to strengthening the financial ecosystem's integrity, improving customer trust, and minimizing economic losses associated with digital payment frauds. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18338034 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | FindMe: AI-based Surveillance System for Missing People Prof. Rashmi Tuptewar Vrushank Bhavsar Tanaya Patil Naman Nagota Lavanya Rathi FindMe Surveillance System Missing People In an increasingly interconnected world, the issue of missing persons has emerged as a critical public safety challenge, often constrained by delayed reporting, fragmented data sharing, and manual video analysis. Conventional surveillance review processes are labor-intensive, time-consuming, and prone to human oversight, which significantly reduces the chances of timely recovery. To address these limitations, this project titled "FindMe: AI-Powered Surveillance System for Missing Person Detection" proposes the design and development of an intelligent, real-time identification framework that integrates facial recognition, deep learning, and computer vision technologies. The proposed system introduces a unified architecture that connects surveillance networks, law enforcement databases, and communication channels into a single AI-driven ecosystem. Leveraging pre-trained Convolutional Neural Network (CNN) models such as VGGFace, FaceNet, and ResNet, the system performs automatic face detection, feature extraction, and similarity matching across live and recorded video feeds. To ensure real-time processing, technologies like OpenCV, TensorFlow, and scalable cloud-based APIs are utilized for efficient computation and streaming analytics. Once a match is detected, automated alerts are instantly generated and transmitted to law enforcement agencies through web dashboards, SMS, and email notifications. The project also emphasizes scalability, accuracy, and ethical considerations in surveillance analytics. Measures such as threshold optimization, false-positive reduction, and dynamic database updates enhance system reliability and minimize misidentification risks. Additionally, privacy-preserving techniques and encryption protocols are integrated to safeguard sensitive data. The framework is tested and validated using publicly available datasets such as LFW (Labeled Faces in the Wild) and VGGFace2, ensuring robustness across diverse demographics and environmental conditions. By transforming traditional surveillance into an intelligent and proactive monitoring network, FindMe aims to revolutionize missing person investigations through automation, rapid information retrieval, and enhanced situational awareness. This solution not only accelerates response times but also demonstrates the potential of AI in augmenting public safety and humanitarian efforts through technology-driven intelligence. This research-driven system aspires to bridge the gap between academic machine learning models and industry-grade fraud prevention mechanisms by enabling scalable, low-latency, and continuously adaptive fraud detection pipelines. Beyond its technical implementation, the project contributes to strengthening the financial ecosystem's integrity, improving customer trust, and minimizing economic losses associated with digital payment frauds. |
| title | FindMe: AI-based Surveillance System for Missing People |
| topic | FindMe Surveillance System Missing People |
| url | https://doi.org/10.5281/zenodo.18338034 |