A robust moving object detection system for an outdoor scene must be able to handle adverse illumination conditions such as sudden illumination changes or lack of illumination in a scene. This is of particular importance for scenarios where active illumination cannot be relied upon. Utilizing infrared and video sensors, we propose a novel sensor fusion algorithm that automatically adapts to the environmental changes that effect sensor measurements. The adaptation is done through a new cooperative coevolutionary algorithm that fuses the scene contextual and statistical information through a physics-based method. Our sensor fusion algorithm maintains high detection rates under a variety of conditions and sensor failure. The results are shown for a full 24 hour diurnal cycle.
['Sohail Nadimi', 'Bir Bhanu']
Physics-Based Cooperative Sensor Fusion for Moving Object Detection