Second-Order Spatial-Temporal Correlation Filters for Visual Tracking

Yufeng Yu, Long Chen, Haoyang He, Jianhui Liu, Weipeng Zhang and Guoxia Xu
Additional contact information
Yufeng Yu: Department of Computer and Information Science, University of Macau, Macau 999078, China
Long Chen: Department of Computer and Information Science, University of Macau, Macau 999078, China
Haoyang He: Department of Statistics, Guangzhou University, Guangzhou 510006, China
Jianhui Liu: Jiangsu Province Key Lab on Image Processing and Image Communication, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Weipeng Zhang: PLA Strategic Support Force, Beijing 450001, China
Guoxia Xu: Department of Computer Science, Norwegian University of Science and Technology, 2815 Gjovik, Norway

Mathematics, 2022, vol. 10, issue 5, 1-15

Abstract: Discriminative correlation filters (DCFs) have been widely used in visual object tracking, but often suffer from two problems: the boundary effect and temporal filtering degradation. To deal with these issues, many DCF-based variants have been proposed and have improved the accuracy of visual object tracking. However, these trackers only adopt first-order data-fitting information and have difficulty maintaining robust tracking in unconstrained scenarios, especially in the case of complex appearance variations. In this paper, by introducing a second-order data-fitting term to the DCF, we propose a second-order spatial–temporal correlation filter (SSCF) learning model. To be specific, the SSCF tracker both incorporates the first-order and second-order data-fitting terms into the DCF framework and makes the learned correlation filter more discriminative. Meanwhile, the spatial–temporal regularization was integrated to develop a robust model in tracking with complex appearance variations. Extensive experiments were conducted on the benchmarking databases CVPR2013, OTB100, DTB70, UAV123, and UAVDT-M. The results demonstrated that our SSCF can achieve competitive performance compared to the state-of-the-art trackers. When penalty parameter λ was set to 10 − 5 , our SSCF gained DP scores of 0.882, 0.868, 0.706, 0.676, and 0.928 on the CVPR2013, OTB100, DTB70, UAV123, and UAVDT-M databases, respectively.

Keywords: correlation filters; second-order fitting; visual tracking (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/5/684/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/5/684/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:5:p:684-:d:756011

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().