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Tangent Fisher Vector on Matrix Manifolds for Action Recognition 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 卷号: 29, 期号: 1, 页码: 3052-3064
作者:  Luo, Guan;  Wei, Jiutong;  Hu, Weiming;  Maybank, Stephen J.
浏览  |  Adobe PDF(2396Kb)  |  收藏  |  浏览/下载：342/75  |  提交时间：2020/04/07
Manifolds  Video sequences  Observability  Videos  Covariance matrices  Kernel  Computational modeling  Action recognition  Fisher vector  Grassmann manifold  Hankel matrix  matrix manifold  
Dual L-1-Normalized Context Aware Tensor Power Iteration and Its Applications to Multi-object Tracking and Multi-graph Matching 期刊论文
INTERNATIONAL JOURNAL OF COMPUTER VISION, 2019, 期号: 128, 页码: 33
作者:  Hu, Weiming;  Shi, Xinchu;  Zhou, Zongwei;  Xing, Junliang;  Ling, Haibin;  Maybank, Stephen
Adobe PDF(6300Kb)  |  收藏  |  浏览/下载：625/219  |  提交时间：2019/12/16
Multi-dimensional assignment  Context  hyper-context aware tensor power iteration  Multi-object tracking  Multi-graph matching  
Online Multi-Target Tracking with Tensor-Based High-Order Graph Matching 会议论文
, 中国，北京, 2018-8-20
作者:  Zhou, Zongwei;  Xing, Junliang;  Zhang, Mengdan;  Hu, Weiming
Adobe PDF(1971Kb)  |  收藏  |  浏览/下载：171/41  |  提交时间：2021/06/23
Learning A Superpixel-Driven Speed Function for Level Set Tracking 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2016, 卷号: 46, 期号: 7, 页码: 1498-1510
作者:  Zhou, Xue;  Li, Xi;  Hu, Weiming
浏览  |  Adobe PDF(2788Kb)  |  收藏  |  浏览/下载：373/121  |  提交时间：2016/10/20
Level Set Tracking  Metric Learning  Non-negative Matrix Factorization (Nf)  Speed Function  Superpixel (Sp)-driven  
A Robust Tracking System for Low Frame Rate Video 期刊论文
INTERNATIONAL JOURNAL OF COMPUTER VISION, 2015, 卷号: 115, 期号: 3, 页码: 279-304
作者:  Zhang, Xiaoqin;  Hu, Weiming;  Xie, Nianhua;  Bao, Hujun;  Maybank, Stephen
浏览  |  Adobe PDF(2788Kb)  |  收藏  |  浏览/下载：292/60  |  提交时间：2016/01/18
Low Frame Rate  Tracking  Dominant Color  Bin-ratio Matching Metric  Particle Swarm Optimization  
Modeling Geometric-Temporal Context With Directional Pyramid Co-Occurrence for Action Recognition 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2014, 卷号: 23, 期号: 2, 页码: 658-672
作者:  Yuan, Chunfeng;  Li, Xi;  Hu, Weiming;  Ling, Haibin;  Maybank, Stephen J.
浏览  |  Adobe PDF(3487Kb)  |  收藏  |  浏览/下载：314/66  |  提交时间：2015/08/12
Covariance Cuboid Descriptor  Log-euclidean Riemannian Metric  Spatio-temporal Directional Pyramid Co-occurrence Matrix  Kernel Machine  Action Recognition  
Action recognition using linear dynamic systems 期刊论文
PATTERN RECOGNITION, PATTERN RECOGNITION, 2013, 2013, 卷号: 46, 46, 期号: 6, 页码: 1710-1718, 1710-1718
作者:  Wang, Haoran;  Yuan, Chunfeng;  Luo, Guan;  Hu, Weiming;  Sun, Changyin
浏览  |  Adobe PDF(648Kb)  |  收藏  |  浏览/下载：604/227  |  提交时间：2015/08/12
Linear Dynamic System  Linear Dynamic System  Kernel Principal Angle  Kernel Principal Angle  Multiclass Spectral Clustering  Multiclass Spectral Clustering  Supervised Codebook Pruning  Supervised Codebook Pruning  Action Recognition  Action Recognition  
Multi-target Tracking by Rank-1 Tensor Approximation 会议论文
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, Oregon, USA, 2013
作者:  Shi, Xinchu;  Ling, Haibin;  Xing, Junliang;  Hu, Weiming;  Junliang Xing
浏览  |  Adobe PDF(916Kb)  |  收藏  |  浏览/下载：201/69  |  提交时间：2015/08/19
Supervised class-specific dictionary learning for sparse modeling in action recognition 期刊论文
PATTERN RECOGNITION, 2012, 卷号: 45, 期号: 11, 页码: 3902-3911
作者:  Wang, Haoran;  Yuan, Chunfeng;  Hu, Weiming;  Sun, Changyin
浏览  |  Adobe PDF(668Kb)  |  收藏  |  浏览/下载：294/109  |  提交时间：2015/08/12
Compound Feature  Motion Segment  Sparse Model  Supervised Dictionary Learning  Action Recognition  
Efficient Clustering Aggregation Based on Data Fragments 期刊论文
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2012, 卷号: 42, 期号: 3, 页码: 913-926
作者:  Wu, Ou;  Hu, Weiming;  Maybank, Stephen J.;  Zhu, Mingliang;  Li, Bing;  Ou Wu
浏览  |  Adobe PDF(798Kb)  |  收藏  |  浏览/下载：288/66  |  提交时间：2015/08/12
Clustering Aggregation  Comparison Measure  Computational Complexity  Data Fragment  Fragment-based Approach  Mutual Information  Point-based Approach