Institutional Repository of Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
| Dense Trajectories and Motion Boundary Descriptors for Action Recognition | |
Wang, Heng1; Klaeser, Alexander2; Schmid, Cordelia2; Liu, Cheng-Lin1
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| Source Publication | INTERNATIONAL JOURNAL OF COMPUTER VISION
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| 2013-05-01 | |
| Volume | 103Issue:1Pages:60-79 |
| Subtype | Article |
| Abstract | This 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. |
| Keyword | Action Recognition Dense Trajectories Motion Boundary Histograms |
| WOS Headings | Science & Technology ; Technology |
| WOS Keyword | OBJECT TRAJECTORIES ; FEATURES ; VIDEO ; CLASSIFICATION ; TEXTURE ; SCALE |
| Indexed By | SCI |
| Language | 英语 |
| WOS Research Area | Computer Science |
| WOS Subject | Computer Science, Artificial Intelligence |
| WOS ID | WOS:000318413500003 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://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 |
| Affiliation | 1.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 Affilication | Chinese 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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