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Dense Trajectories and Motion Boundary Descriptors for Action Recognition

International journal of computer vision, 2013-05, Vol.103 (1), p.60-79 [Peer Reviewed Journal]

Springer Science+Business Media New York 2013 ;2014 INIST-CNRS ;COPYRIGHT 2013 Springer ;Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 0920-5691 ;EISSN: 1573-1405 ;DOI: 10.1007/s11263-012-0594-8

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Title:
Dense Trajectories and Motion Boundary Descriptors for Action Recognition
Author: Wang, Heng ; Kläser, Alexander ; Schmid, Cordelia ; Liu, Cheng-Lin
Subjects: 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
Is Part Of: International journal of computer vision, 2013-05, Vol.103 (1), p.60-79
Description: 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.
Publisher: Boston: Springer US
Language: English
Identifier: ISSN: 0920-5691
EISSN: 1573-1405
DOI: 10.1007/s11263-012-0594-8
Source: AUTh Library subscriptions: ProQuest Central
Hyper Article en Ligne (HAL) (Open Access)
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Dense Trajectories and Motion Boundary Descriptors for Action Recognition

Springer Science+Business Media New York 2013 ;2014 INIST-CNRS ;COPYRIGHT 2013 Springer ;Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 0920-5691 ;EISSN: 1573-1405 ;DOI: 10.1007/s11263-012-0594-8

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