Abstract
Visual object tracking is a challenging task in computer vision. Recently, due to high efficiency and performance, correlation filter-based trackers have attracted much attention. In this paper, we propose a collaborative correlation filter tracking framework. First, we learn multiple kernelized correlation filters for different features independently, and fuse their response maps to obtain a more reliable response map for translation estimation. Then, we learn a scale correlation filter to handle the scale variation. Moreover, in order to further improve the tracking accuracy, we build an online detector to re-detect objects in local neighbor region, when the tracking result is unreliable. Extensive evaluations on the recent benchmark datasets show that the proposed algorithm performs favorably against several state-of-the-art methods.
Sign-in/Register to access full text options 
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.
