{"type":"ScholarlyArticle","title":"Feature Integration with Adaptive Importance Maps for Visual Tracking","authors":[{"name":"Aishi Li","slug":"aishi-li","given":"Aishi","family":"Li","affiliation":"Nanjing Normal University"},{"name":"Ming Yang","slug":"ming-yang","given":"Ming","family":"Yang","affiliation":"Nanjing Normal University"},{"name":"Wanqi Yang","slug":"wanqi-yang","given":"Wanqi","family":"Yang","affiliation":"Nanjing Normal University"}],"abstract":"Discriminative correlation filters have recently achieved excellent performance for visual object tracking. The key to success is to make full use of dense sampling and specific properties of circulant matrices in the Fourier domain. However, previous studies don't take into consideration the importance and complementary information of different features, simply concatenating them. This paper investigates an effective method of feature integration for correlation filters, which jointly learns filters, as well as importance maps in each frame. These importance maps borrow the advantages of different features, aiming to achieve complementary traits and improve robustness. Moreover, for each feature, an importance map is shared by its all channels to avoid overfitting. In addition, we introduce a regularization term for the importance maps and use the penalty factor to control the significance of features. Based on handcrafted and CNN features, we implement two trackers, which achieve a competitive performance compared with several state-of-the-art trackers.","doi":"10.24963/ijcai.2018/108","url":"https://ijcai.org/proceedings/2018/108","pdf":"https://ijcai.org/proceedings/2018/0108.pdf","markdown":"https://ijcai.org/proceedings/2018/108.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/108","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":108,"pages":{"first":779,"last":785},"keywords":["Computer Vision: Motion and Tracking","Computer Vision: Computer Vision"],"videos":[],"references":[{"position":1,"raw":"[Bertinetto et al., 2016a] Luca Bertinetto, Jack Valmadre, S- tuart Golodetz, Ondrej Miksik, and Philip HS Torr. 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