Novel Perceptive Approach for Automation on Ideal Self-Regulating Video Surveillance Model
Keywords:
Data Analytics, AWS, ML, S3, IoT, Kinesis, Glue, Automation Video SurveillanceAbstract
Video surveillance systems are essential for public safety and security. Traditional surveillance systems, on the other hand, have limited effectiveness and necessitate extensive human interaction and resources. This research presents a fresh perceptive technique for automation on an ideal self-regulating video surveillance model to address these limitations. The suggested model employs cutting-edge computer vision, machine learning, and deep learning techniques to extract pertinent data from video feeds, identify possible threats and irregularities, and adjust surveillance parameters on its own. Our findings show that the self-regulating model can adapt to changing environments and optimize its surveillance parameters to detect potential threats and anomalies accurately and in real time, resulting in several advantages such as improved accuracy, reduced resource requirements, and real-time analytics for rapid response to potential threats. Our report also offers potential research directions to improve the surveillance system's security and scalability, such as investigating the integration of modern technologies like blockchain and edge computing. Overall, the suggested self-regulating video surveillance model represents a substantial improvement in video surveillance and public safety, with the potential to improve public safety and security in a variety of businesses and circumstances.
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