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Tangent Fisher Vector on Matrix Manifolds for Action Recognition | |
Luo, Guan1![]() ![]() ![]() | |
发表期刊 | IEEE TRANSACTIONS ON IMAGE PROCESSING
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ISSN | 1057-7149 |
2020 | |
卷号 | 29期号:1页码:3052-3064 |
摘要 | In this paper, we address the problem of representing and recognizing human actions from videos on matrix manifolds. For this purpose, we propose a new vector representation method, named tangent Fisher vector, to describe video sequences in the Fisher kernel framework. We first extract dense curved spatio-temporal cuboids from each video sequence. Compared with the traditional 'straight cuboids', the dense curved spatio-temporal cuboids contain much more local motion information. Each cuboid is then described using a linear dynamical system (LDS) to simultaneously capture the local appearance and dynamics. Furthermore, a simple yet efficient algorithm is proposed to learn the LDS parameters and approximate the observability matrix at the same time. Each video sequence is thus represented by a set of LDSs. Considering that each LDS can be viewed as a point in a Grassmann manifold, we propose to learn an intrinsic GMM on the manifold to cluster the LDS points. Finally a tangent Fisher vector is computed by first accumulating all the tangent vectors in each Gaussian component, and then concatenating the normalized results across all the Gaussian components. A kernel is defined to measure the similarity between tangent Fisher vectors for classification and recognition of a video sequence. This approach is evaluated on the state-of-the-art human action benchmark datasets. The recognition performance is competitive when compared with current state-of-the-art results. |
关键词 | Manifolds Video sequences Observability Videos Covariance matrices Kernel Computational modeling Action recognition Fisher vector Grassmann manifold Hankel matrix matrix manifold |
DOI | 10.1109/TIP.2019.2955561 |
关键词[WOS] | BINET-CAUCHY KERNELS ; DYNAMICAL-SYSTEMS ; VIEW ; MODELS ; VIDEO ; IDENTIFICATION ; CLASSIFICATION ; DESCRIPTORS |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Key Research Program of Frontier Sciences, CAS[QYZDJSSW-JSC040] ; NSFC-general technology collaborative Fund for basic research[U1636218] ; National Natural Science Foundation of Guangdong[2018B030311046] ; CAS External Cooperation Key Project ; Natural Science Foundation of China[61721004] ; Natural Science Foundation of China[61751212] ; Beijing Natural Science Foundation[L172051] ; Beijing Natural Science Foundation[L172051] ; Natural Science Foundation of China[61751212] ; Natural Science Foundation of China[61721004] ; CAS External Cooperation Key Project ; National Natural Science Foundation of Guangdong[2018B030311046] ; NSFC-general technology collaborative Fund for basic research[U1636218] ; Key Research Program of Frontier Sciences, CAS[QYZDJSSW-JSC040] |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000510750900013 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 图像视频处理与分析 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/38370 |
专题 | 多模态人工智能系统全国重点实验室_视频内容安全 |
通讯作者 | Hu, Weiming |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Birkbeck Coll, Dept Comp Sci & Informat Syst, London WC1E 7HX, England |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Luo, Guan,Wei, Jiutong,Hu, Weiming,et al. Tangent Fisher Vector on Matrix Manifolds for Action Recognition[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2020,29(1):3052-3064. |
APA | Luo, Guan,Wei, Jiutong,Hu, Weiming,&Maybank, Stephen J..(2020).Tangent Fisher Vector on Matrix Manifolds for Action Recognition.IEEE TRANSACTIONS ON IMAGE PROCESSING,29(1),3052-3064. |
MLA | Luo, Guan,et al."Tangent Fisher Vector on Matrix Manifolds for Action Recognition".IEEE TRANSACTIONS ON IMAGE PROCESSING 29.1(2020):3052-3064. |
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