{"type":"ScholarlyArticle","title":"CFNN: Correlation Filter Neural Network for Visual Object Tracking","authors":[{"name":"Yang Li","slug":"yang-li","given":"Yang","family":"Li","affiliation":"Zhejiang University"},{"name":"Zhan Xu","slug":"zhan-xu","given":"Zhan","family":"Xu","affiliation":"University of Massachusetts Amherst"},{"name":"Jianke Zhu","slug":"jianke-zhu","given":"Jianke","family":"Zhu","affiliation":"Zhejiang University; Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies 1 ,; Zhejiang University."}],"abstract":"Albeit convolutional neural network (CNN) has shown promising capacity in many computer vision tasks, applying it to visual tracking is yet far from solved. Existing methods either employ a large external dataset to undertake exhaustive pre-training or suffer from less satisfactory results in terms of accuracy and robustness. To track single target in a wide range of videos, we present a novel Correlation Filter Neural Network architecture, as well as a complete visual tracking pipeline,  The proposed approach is a special case of CNN, whose initialization does not need any pre-training on the external dataset. The initialization of network enjoys the merits of cyclic sampling to achieve the appealing discriminative capability, while the network updating scheme adopts advantages from back-propagation in order to capture new appearance variations. The tracking pipeline integrates both aspects well by making them complementary to each other. We validate our tracker on OTB-2013 benchmark. The proposed tracker obtains the promising results compared to most of existing representative trackers.","doi":"10.24963/ijcai.2017/309","url":"https://ijcai.org/proceedings/2017/309","pdf":"https://ijcai.org/proceedings/2017/0309.pdf","markdown":"https://ijcai.org/proceedings/2017/309.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/309","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning A-R","track":"Main track","number":309,"pages":{"first":2222,"last":2229},"keywords":["Machine Learning: Neural Networks","Robotics and Vision: Vision and Perception"],"videos":[],"references":[{"position":1,"raw":"[Bertinetto et al., 2016] Luca Bertinetto, Jack Valmadre, J.F Henriques, Andrea Vedaldi, and Philip HS Torr. 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