Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
