Build a smart surveillance system that processes video feeds at edge devices to detect intrusions in real time. The system will minimize bandwidth usage, enhance privacy, and provide faster threat detection compared to centralized cloud-based surveillance architectures.
Research video analytics techniques used in smart surveillance.
Design a distributed edge architecture for camera-based monitoring.
Implement real-time motion detection and facial recognition at the edge.
Use lightweight deep learning models optimized for edge deployment.
Configure local alert systems for unauthorized access detection.
Implement selective cloud upload only for suspicious activities.
Optimize video compression techniques to reduce network load.
Compare detection latency between edge and cloud processing.
Develop a web-based interface for viewing alerts and reports.
Perform testing in simulated indoor and outdoor environments.
Evaluate privacy improvements through local data processing.
Document system efficiency in terms of processing speed and bandwidth savings.
