Affiliations: [a] College of Electric and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, China
| [b] Nanyang Cigarette Factory, China Tobacco Henan Industrial Co., Ltd., Nanyang, Henan, China
Correspondence:
[*]
Corresponding author. Huanlong Zhang, College of Electric and Information Engineering, Zhengzhou University of Light Industry, No. 5 Dongfeng Road, Zhengzhou, China. E-mail: zhl_lit@163.com and Guanglu Yang, Nanyang Cigarette Factory, China Tobacco Henan Industrial Co., Ltd., Nanyang 473007, Henan, China.
78600322@qq.com.
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.
Keywords: Kernel correlation filter, moth-flame optimization, fast motion, visual tracking
