Ai Powered Smart Surveillance With Foreign Face Detection For Efficient Cloud Data Management

Abstract: In the modern era of digital surveillance, organizations face an overwhelming challenge in managing and storing the vast amounts of video data generated from surveillance systems. Traditional methods that rely on indiscriminate data storage not only place significant burdens on cloud infrastructure but also result in excessive energy consumption and costs. The project, AI-Powered Smart Surveillance with Foreign Face Detection for Efficient Cloud Data Management, introduces an innovative solution to address these issues through the integration of advanced AI-driven technologies for foreign face detection, local processing, and selective cloud storage. The system continuously monitors real-time video feeds from cameras, utilizing OpenCV for motion detection and Dlib-based algorithms for face tracking and recognition. Upon detecting a face, the system cross-references it with a local database of known individuals using a locally stored face recognition model. If the face is identified as foreign (i.e., not in the database), the system triggers an alert and flags the footage for upload to the cloud. By uploading only the flagged footage featuring foreign or suspicious individuals this solution eliminates the need for storing vast amounts of irrelevant footage, thereby reducing both cloud storage demands and data transmission bandwidth. A key differentiator of this solution is the implementation of edge computing, where all data processing tasks such as face detection, recognition, and motion analysis are carried out locally on the edge device This eliminates the latency and inefficiencies associated with continuous cloud-based processing. The use of Python enables real- time data handling, while TensorFlow enhances the flexibility and accuracy of face detection models, ensuring quick identification of foreign faces. From an environmental perspective, our system makes significant strides in promoting sustainability. By reducing the volume of video data uploaded to the cloud, we directly lower the energy consumption of data centers widely recognized as major contributors to global carbon emissions. This reduction in storage requirements not only results in substantial cost savings for organizations but also aligns with global efforts to reduce carbon footprints and enhance energy efficiency in technology infrastructures. In summary, this project represents a groundbreaking approach to smart surveillance by combining AI-powered facial recognition with efficient cloud management. The solution optimizes resource use, minimizes operational costs, and contributes to global sustainability initiatives, making it ideal for adoption by environmentally conscious enterprises. By leveraging state-of-the-art technologies like OpenCV, Dlib, TensorFlow, and edge computing, our system offers a scalable, intelligent, and eco-friendly alternative to conventional surveillance systems

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