CASIA OpenIR  > 模式识别国家重点实验室  > 模式分析与学习
Dense Trajectories and Motion Boundary Descriptors for Action Recognition
Wang, Heng1; Klaeser, Alexander2; Schmid, Cordelia2; Liu, Cheng-Lin1
Source PublicationINTERNATIONAL JOURNAL OF COMPUTER VISION
2013-05-01
Volume103Issue:1Pages:60-79
SubtypeArticle
AbstractThis paper introduces a video representation based on dense trajectories and motion boundary descriptors. Trajectories capture the local motion information of the video. A dense representation guarantees a good coverage of foreground motion as well as of the surrounding context. A state-of-the-art optical flow algorithm enables a robust and efficient extraction of dense trajectories. As descriptors we extract features aligned with the trajectories to characterize shape (point coordinates), appearance (histograms of oriented gradients) and motion (histograms of optical flow). Additionally, we introduce a descriptor based on motion boundary histograms (MBH) which rely on differential optical flow. The MBH descriptor shows to consistently outperform other state-of-the-art descriptors, in particular on real-world videos that contain a significant amount of camera motion. We evaluate our video representation in the context of action classification on nine datasets, namely KTH, YouTube, Hollywood2, UCF sports, IXMAS, UIUC, Olympic Sports, UCF50 and HMDB51. On all datasets our approach outperforms current state-of-the-art results.
KeywordAction Recognition Dense Trajectories Motion Boundary Histograms
WOS HeadingsScience & Technology ; Technology
WOS KeywordOBJECT TRAJECTORIES ; FEATURES ; VIDEO ; CLASSIFICATION ; TEXTURE ; SCALE
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000318413500003
Citation statistics
Cited Times:707[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/3075
Collection模式识别国家重点实验室_模式分析与学习
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
2.INRIA Grenoble Rhone Alpes, LEAR Team, F-38330 Montbonnot St Martin, France
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Wang, Heng,Klaeser, Alexander,Schmid, Cordelia,et al. Dense Trajectories and Motion Boundary Descriptors for Action Recognition[J]. INTERNATIONAL JOURNAL OF COMPUTER VISION,2013,103(1):60-79.
APA Wang, Heng,Klaeser, Alexander,Schmid, Cordelia,&Liu, Cheng-Lin.(2013).Dense Trajectories and Motion Boundary Descriptors for Action Recognition.INTERNATIONAL JOURNAL OF COMPUTER VISION,103(1),60-79.
MLA Wang, Heng,et al."Dense Trajectories and Motion Boundary Descriptors for Action Recognition".INTERNATIONAL JOURNAL OF COMPUTER VISION 103.1(2013):60-79.
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