Robust real-time intrusion detection with fuzzy classification
We propose a novel system for indoor video surveillance. It is able to detect and track moving objects even in the presence of significant variations of scene illumination. After a preliminary analysis and clustering of temporal changes in the video sequence, the algorithm performs a classification based on fuzzy logic, aimed at identifying moving regions that really correspond to unexpected objects in the scene. The proposed approach tends to discard shadows, reflections and luminance profile changes due to illumination variations. One key feature of our system is its modest computation complexity, which allows it to operate in real-time on a common PC platform. The system has been tested on a wide variety of situations, proving its effectiveness and robustness.