Abstract for an Intelligent Video Surveillance System using Deep Learning in Python:
This project proposes the development of an Intelligent Video Surveillance System utilizing deep learning techniques implemented in Python. The system aims to enhance traditional video surveillance by incorporating advanced algorithms that can intelligently analyze and interpret video feeds in real-time. Deep learning models, such as Convolutional Neural Networks (CNNs) and recurrent neural networks (RNNs), will be employed to detect and recognize objects, track movements, and identify potential anomalies or security threats. The integration of deep learning allows for more accurate and context-aware surveillance, reducing false alarms and improving overall system reliability. The proposed system demonstrates the potential for a sophisticated and automated video surveillance solution with enhanced capabilities for threat detection and prevention.