H
Hamed Kiani Galoogahi
Researcher at Carnegie Mellon University
Publications - 22
Citations - 3629
Hamed Kiani Galoogahi is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Video tracking & Optical flow. The author has an hindex of 16, co-authored 22 publications receiving 2714 citations. Previous affiliations of Hamed Kiani Galoogahi include Istituto Italiano di Tecnologia & National University of Singapore.
Papers
More filters
Posted Content
Learning Background-Aware Correlation Filters for Visual Tracking
TL;DR: In this article, a background-aware correlation filter is proposed to model how both the foreground and background of the object varies over time, which can be used for real-time tracking.
Proceedings ArticleDOI
Learning Background-Aware Correlation Filters for Visual Tracking
TL;DR: This work proposes a Background-Aware CF based on hand-crafted features (HOG] that can efficiently model how both the foreground and background of the object varies over time, and superior accuracy and real-time performance of the method compared to the state-of-the-art trackers.
Book ChapterDOI
The sixth visual object tracking VOT2018 challenge results
Matej Kristan,Ales Leonardis,Jiří Matas,Michael Felsberg,Roman Pflugfelder,Roman Pflugfelder,Luka Čehovin Zajc,Tomas Vojir,Goutam Bhat,Alan Lukežič,Abdelrahman Eldesokey,Gustavo Fernandez,Alvaro Garcia-Martin,Álvaro Iglesias-Arias,A. Aydin Alatan,Abel Gonzalez-Garcia,Alfredo Petrosino,Alireza Memarmoghadam,Andrea Vedaldi,Andrej Muhič,Anfeng He,Arnold W. M. Smeulders,Asanka G. Perera,Bo Li,Boyu Chen,Changick Kim,Changsheng Xu,Changzhen Xiong,Cheng Tian,Chong Luo,Chong Sun,Cong Hao,Daijin Kim,Deepak Mishra,Deming Chen,Dong Wang,Dongyoon Wee,Efstratios Gavves,Erhan Gundogdu,Erik Velasco-Salido,Fahad Shahbaz Khan,Fan Yang,Fei Zhao,Feng Li,Francesco Battistone,George De Ath,Gorthi R. K. Sai Subrahmanyam,Guilherme Sousa Bastos,Haibin Ling,Hamed Kiani Galoogahi,Hankyeol Lee,Haojie Li,Haojie Zhao,Heng Fan,Honggang Zhang,Horst Possegger,Houqiang Li,Huchuan Lu,Hui Zhi,Huiyun Li,Hyemin Lee,Hyung Jin Chang,Isabela Drummond,Jack Valmadre,Jaime Spencer Martin,Javaan Chahl,Jin-Young Choi,Jing Li,Jinqiao Wang,Jinqing Qi,Jinyoung Sung,Joakim Johnander,João F. Henriques,Jongwon Choi,Joost van de Weijer,Jorge Rodríguez Herranz,Jorge Rodríguez Herranz,José M. Martínez,Josef Kittler,Junfei Zhuang,Junyu Gao,Klemen Grm,Lichao Zhang,Lijun Wang,Lingxiao Yang,Litu Rout,Liu Si,Luca Bertinetto,Lutao Chu,Manqiang Che,Mario Edoardo Maresca,Martin Danelljan,Ming-Hsuan Yang,Mohamed H. Abdelpakey,Mohamed Shehata,Myunggu Kang,Namhoon Lee,Ning Wang,Ondrej Miksik,Payman Moallem,Pablo Vicente-Moñivar,Pedro Senna,Peixia Li,Philip H. S. Torr,Priya Mariam Raju,Qian Ruihe,Qiang Wang,Qin Zhou,Qing Guo,Rafael Martin-Nieto,Rama Krishna Sai Subrahmanyam Gorthi,Ran Tao,Richard Bowden,Richard M. Everson,Runling Wang,Sangdoo Yun,Seokeon Choi,Sergio Vivas,Shuai Bai,Shuangping Huang,Sihang Wu,Simon Hadfield,Siwen Wang,Stuart Golodetz,Tang Ming,Tianyang Xu,Tianzhu Zhang,Tobias Fischer,Vincenzo Santopietro,Vitomir Struc,Wang Wei,Wangmeng Zuo,Wei Feng,Wei Wu,Wei Zou,Weiming Hu,Wengang Zhou,Wenjun Zeng,Xiaofan Zhang,Xiaohe Wu,Xiaojun Wu,Xinmei Tian,Yan Li,Yan Lu,Yee Wei Law,Yi Wu,Yi Wu,Yiannis Demiris,Yicai Yang,Yifan Jiao,Yuhong Li,Yuhong Li,Yunhua Zhang,Yuxuan Sun,Zheng Zhang,Zheng Zhu,Zhen-Hua Feng,Zhihui Wang,Zhiqun He +158 more
TL;DR: The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative; results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years.
Proceedings ArticleDOI
Correlation filters with limited boundaries
TL;DR: This paper proposes a novel approach to correlation filter estimation that takes advantage of inherent computational redundancies in the frequency domain, dramatically reduces boundary effects, and is able to implicitly exploit all possible patches densely extracted from training examples during learning process.
Proceedings ArticleDOI
Multi-channel Correlation Filters
TL;DR: A novel framework for learning a multi-channel detector/filter efficiently in the frequency domain, both in terms of training time and memory footprint is proposed, which is referred to as a multichannel correlation filter.