Improvement of a neural-fuzzy motion detection vision model for complex scenario conditions

Motion detection represents a challenging issue in artificial vision systems. Besides detection of movement in normal scenario conditions robust systems must deal with other non-normal conditions. We propose the improvement of a former neuro-fuzzy motion detection method to face drastic illumination changes, gradual illumination conditions, moving background and scene composition changes. The improvements include adaptive learning rates as well as the inclusion of new fuzzy rules. Experimental findings over several video sequences verify that the improvements outperform the performance of the original method in the non-normal conditions without affecting the performance under normal conditions.