{"type":"ScholarlyArticle","title":"Learning Robust Gaussian Process Regression for Visual Tracking","authors":[{"name":"Linyu Zheng","slug":"linyu-zheng","given":"Linyu","family":"Zheng","affiliation":"University of Chinese Academy of Sciences , Beijing , China; National Lab of Pattern Recognition , Institute of Automation , CAS , Beijing 100190 , China"},{"name":"Ming Tang","slug":"ming-tang","given":"Ming","family":"Tang","affiliation":"University of Chinese Academy of Sciences , Beijing , China; National Lab of Pattern Recognition , Institute of Automation , CAS , Beijing 100190 , China"},{"name":"Jinqiao Wang","slug":"jinqiao-wang","given":"Jinqiao","family":"Wang","affiliation":"University of Chinese Academy of Sciences , Beijing , China; National Lab of Pattern Recognition , Institute of Automation , CAS , Beijing 100190 , China"}],"abstract":"Recent developments of Correlation Filter based trackers (CF trackers) have attracted much attention because of their top performance. However, the boundary effect imposed by the basic periodic assumption in their fast optimization seriously degrades the performance of CF trackers. Although there existed many recent works to relax the boundary effect in CF trackers, the cost was that they can not utilize the kernel trick to improve the accuracy further. In this paper, we propose a novel Gaussian Process Regression based tracker (GPRT) which is a conceptually natural tracking approach. Compared to all the existing CF trackers, the boundary effect is eliminated thoroughly and the kernel trick can be employed in our GPRT. In addition, we present two efficient and effective update methods for our GPRT. Experiments are performed on two public datasets: OTB-2013 and OTB-2015. Without bells and whistles, on these two datasets, our GPRT obtains 84.1% and 79.2% in mean overlap precision, respectively, outperforming all the existing trackers with hand-crafted features.","doi":"10.24963/ijcai.2018/170","url":"https://ijcai.org/proceedings/2018/170","pdf":"https://ijcai.org/proceedings/2018/0170.pdf","markdown":"https://ijcai.org/proceedings/2018/170.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/170","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Computer Vision","track":"Main track","number":170,"pages":{"first":1219,"last":1225},"keywords":["Computer Vision: Motion and Tracking","Computer Vision: Computer Vision"],"videos":[],"references":[{"position":1,"raw":"[Bertinetto et al., 2016] Luca Bertinetto, Jack Valmadre, S- tuart Golodetz, Ondrej Miksik, and Philip HS Torr. 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