SUSPICIOUS HUMAN ACTIVITY TRACKING AI CAMERA USING BLACK BOX

The rapid advancements in artificial intelligence (AI) have paved the way for innovative applications in surveillance and security systems. This paper introduces a novel approach to suspicious human activity tracking using an AI-powered camera system integrated with a black box for enhanced functionality and privacy preservation. The proposed system employs deep learning algorithms to analyze real-time video streams and detect anomalies in human behavior that may indicate potential security threats. The AI camera utilizes convolutional neural network (CNN) architecture for efficient object detection and tracking. The model is trained on a diverse dataset to recognize normal and suspicious activities based on motion patterns, object interactions, and spatial relationships. To augment the system's adaptability, a black box component is introduced, encapsulating the core AI algorithms and ensuring a transparent and auditable decision-making process. The black box serves multiple purposes, including safeguarding sensitive information, addressing ethical concerns related to privacy, and facilitating regulatory compliance. It acts as an isolated module that processes and interprets video feeds without exposing raw data or compromising individual privacy. The integration of the black box also enables the logging of decision-making processes, contributing to accountability and traceability in the system's operations. Furthermore, the proposed system incorporates a feedback mechanism, allowing users to provide input to the black box to improve the AI model's accuracy over time. This iterative learning approach enhances the system's ability to adapt to evolving threats and reduces the risk of false positives or negatives.