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Weiming Hu
Researcher at Chinese Academy of Sciences
Publications - 422
Citations - 27373
Weiming Hu is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Video tracking & Feature extraction. The author has an hindex of 62, co-authored 386 publications receiving 22322 citations. Previous affiliations of Weiming Hu include Center for Excellence in Education & University of California, San Diego.
Papers
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Journal ArticleDOI
A survey on visual surveillance of object motion and behaviors
TL;DR: This paper reviews recent developments and general strategies of the processing framework of visual surveillance in dynamic scenes, and analyzes possible research directions, e.g., occlusion handling, a combination of two and three-dimensional tracking, and fusion of information from multiple sensors, and remote surveillance.
Journal ArticleDOI
Silhouette analysis-based gait recognition for human identification
TL;DR: A simple but efficient gait recognition algorithm using spatial-temporal silhouette analysis is proposed that implicitly captures the structural and transitional characteristics of gait.
Proceedings ArticleDOI
Fast Online Object Tracking and Segmentation: A Unifying Approach
TL;DR: This method improves the offline training procedure of popular fully-convolutional Siamese approaches for object tracking by augmenting their loss with a binary segmentation task, and operates online, producing class-agnostic object segmentation masks and rotated bounding boxes at 55 frames per second.
Journal ArticleDOI
Recent developments in human motion analysis
Liang Wang,Weiming Hu,Tieniu Tan +2 more
TL;DR: This paper provides a comprehensive survey of research on computer-vision-based human motion analysis, namely human detection, tracking and activity understanding, and various methods for each issue are discussed in order to examine the state of the art.
Book ChapterDOI
The Visual Object Tracking VOT2016 Challenge Results
Matej Kristan,Ales Leonardis,Jiří Matas,Michael Felsberg,Roman Pflugfelder,Luka Cehovin,Tomas Vojir,Gustav Häger,Alan Lukežič,Gustavo Fernandez,Abhinav Gupta,Alfredo Petrosino,Alireza Memarmoghadam,Alvaro Garcia-Martin,Andres Solis Montero,Andrea Vedaldi,Andreas Robinson,Andy J. Ma,Anton Varfolomieiev,A. Aydin Alatan,Aykut Erdem,Bernard Ghanem,Bin Liu,Bohyung Han,Brais Martinez,Chang-Ming Chang,Changsheng Xu,Chong Sun,Daijin Kim,Dapeng Chen,Dawei Du,Deepak Mishra,Dit-Yan Yeung,Erhan Gundogdu,Erkut Erdem,Fahad Shahbaz Khan,Fatih Porikli,Fatih Porikli,Fei Zhao,Filiz Bunyak,Francesco Battistone,Gao Zhu,Giorgio Roffo,Gorthi R. K. Sai Subrahmanyam,Guilherme Sousa Bastos,Guna Seetharaman,Henry Medeiros,Hongdong Li,Honggang Qi,Horst Bischof,Horst Possegger,Huchuan Lu,Hyemin Lee,Hyeonseob Nam,Hyung Jin Chang,Isabela Drummond,Jack Valmadre,Jae-chan Jeong,Jaeil Cho,Jae-Yeong Lee,Jianke Zhu,Jiayi Feng,Jin Gao,Jin-Young Choi,Jingjing Xiao,Ji-Wan Kim,Jiyeoup Jeong,João F. Henriques,Jochen Lang,Jongwon Choi,José M. Martínez,Junliang Xing,Junyu Gao,Kannappan Palaniappan,Karel Lebeda,Ke Gao,Krystian Mikolajczyk,Lei Qin,Lijun Wang,Longyin Wen,Luca Bertinetto,Madan Kumar Rapuru,Mahdieh Poostchi,Mario Edoardo Maresca,Martin Danelljan,Matthias Mueller,Mengdan Zhang,Michael Arens,Michel Valstar,Ming Tang,Mooyeol Baek,Muhammad Haris Khan,Naiyan Wang,Nana Fan,Noor M. Al-Shakarji,Ondrej Miksik,Osman Akin,Payman Moallem,Pedro Senna,Philip H. S. Torr,Pong C. Yuen,Qingming Huang,Qingming Huang,Rafael Martin-Nieto,Rengarajan Pelapur,Richard Bowden,Robert Laganiere,Rustam Stolkin,Ryan Walsh,Sebastian B. Krah,Shengkun Li,Shengping Zhang,Shizeng Yao,Simon Hadfield,Simone Melzi,Siwei Lyu,Siyi Li,Stefan Becker,Stuart Golodetz,Sumithra Kakanuru,Sunglok Choi,Tao Hu,Thomas Mauthner,Tianzhu Zhang,Tony P. Pridmore,Vincenzo Santopietro,Weiming Hu,Wenbo Li,Wolfgang Hübner,Xiangyuan Lan,Xiaomeng Wang,Xin Li,Yang Li,Yiannis Demiris,Yifan Wang,Yuankai Qi,Zejian Yuan,Zexiong Cai,Zhan Xu,Zhenyu He,Zhizhen Chi +140 more
TL;DR: The Visual Object Tracking challenge VOT2016 goes beyond its predecessors by introducing a new semi-automatic ground truth bounding box annotation methodology and extending the evaluation system with the no-reset experiment.