Abstract
Kernel Correlation Filter (KCF) tracker has shown great potential on precision, robustness and efficiency. However, the candidate region used to train the correlation filter is fixed, so tracking is difficult when the target escapes from the search window due to fast motion. In this paper, an improved KCF is put forward for long-term tracking. At first, the moth-flame optimization (MFO) algorithm is introduced into tracking to search for lost target. Then, the candidate sample strategy of KCF tracking method is adjusted by MFO algorithm to make it has the capability of fast motion tracking. Finally, we use the conservative learning correlation filter to judge the moving state of the target, and combine the improved KCF tracker to form a unified tracking framework. The proposed algorithm is tested on a self-made dataset benchmark. Moreover, our method obtains scores for both the distance precision plot (0.891 and 0.842) and overlap success plots (0.631 and 0.601) on the OTB-2013 and OTB-2015 data sets, respectively. The results demonstrate the feasibility and effectiveness compared with the state-of-the-art methods, especially in dealing with fast or uncertain motion.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: 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.