Dense Trajectories and Motion Boundary Descriptors for Action Recognition

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 o...

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Veröffentlicht in:	International journal of computer vision Jg. 103; H. 1; S. 60 - 79
Hauptverfasser:	Wang, Heng, Kläser, Alexander, Schmid, Cordelia, Liu, Cheng-Lin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Boston Springer US 01.05.2013 Springer Springer Nature B.V Springer Verlag
Schlagworte:	Algorithms Analysis Applied sciences Artificial Intelligence Boundaries Cameras Classification Computer Imaging Computer Science Computer science; control theory; systems Computer Vision and Pattern Recognition Datasets Exact sciences and technology Histograms Image Processing and Computer Vision Image processing systems Pattern Recognition Pattern Recognition and Graphics Pattern recognition. Digital image processing. Computational geometry Recognition Representations Sensors Spacetime State of the art Studies Trajectories Vision Vision systems Action recognition Motion boundary histograms Dense trajectories Multimedia Computer vision Action Histogram Motion estimation Point shape Video signal Mobility Sport Behavioral analysis Scene analysis Classification Object detection Trajectory Web site Pattern extraction Optical flow
ISSN:	0920-5691, 1573-1405
Online-Zugang:	Volltext
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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.
AbstractList	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. 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 stateof- 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. 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. Keywords Action recognition * Dense trajectories * Motion boundary histograms 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.[PUBLICATION ABSTRACT]
Audience	Academic
Author	Liu, Cheng-Lin Wang, Heng Schmid, Cordelia Kläser, Alexander
Author_xml	– sequence: 1 givenname: Heng surname: Wang fullname: Wang, Heng email: hengwang00@gmail.com, hwang@nlpr.ia.ac.cn organization: National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences – sequence: 2 givenname: Alexander surname: Kläser fullname: Kläser, Alexander organization: LEAR Team, INRIA Grenoble Rhône-Alpes – sequence: 3 givenname: Cordelia surname: Schmid fullname: Schmid, Cordelia organization: LEAR Team, INRIA Grenoble Rhône-Alpes – sequence: 4 givenname: Cheng-Lin surname: Liu fullname: Liu, Cheng-Lin organization: National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
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Keywords	Action recognition Motion boundary histograms Dense trajectories Multimedia Computer vision Action Histogram Motion estimation Point shape Video signal Mobility Sport Behavioral analysis Scene analysis Classification Object detection Trajectory Web site Pattern extraction Optical flow
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Snippet	This paper introduces a video representation based on dense trajectories and motion boundary descriptors. Trajectories capture the local motion information of...
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SubjectTerms	Algorithms Analysis Applied sciences Artificial Intelligence Boundaries Cameras Classification Computer Imaging Computer Science Computer science; control theory; systems Computer Vision and Pattern Recognition Datasets Exact sciences and technology Histograms Image Processing and Computer Vision Image processing systems Pattern Recognition Pattern Recognition and Graphics Pattern recognition. Digital image processing. Computational geometry Recognition Representations Sensors Spacetime State of the art Studies Trajectories Vision Vision systems
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Title	Dense Trajectories and Motion Boundary Descriptors for Action Recognition
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