Augmented Home Security System with Computer Vision Based Human Activity Recognition

The project pioneers a Augmented Home Security System with Computer Vision Based Human Activity Recognition, addressing shortcomings in current surveillance practices to fortify residential protection. In response to escalating demands for advanced security, the study identifies limitations in conventional methods. Leveraging insights from previous research on computer vision and deep learning, the primary objective is to implement cutting-edge image analysis, particularly employing CNN, and even instantaneous categorization of human activities. With a meticulously curated dataset, up-to-date Deep Learning (DL) models are trained, enabling robust recognition. The methodological approach involves real-time video feed analysis, empowering the system to discern a wide spectrum of activities. Beyond elevating home security, this research contributes substantively to the evolving intersection of computer vision and residential protection. Anticipated outcomes include an intelligent and responsive home security solution poised to redefine residential safety standards.