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
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Beschreibung
Zusammenfassung: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.
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ISSN:0920-5691
1573-1405
DOI:10.1007/s11263-012-0594-8