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Dense trajectories and motion boundary descriptors for action recognition

Heng Wang 1, * Alexander Kläser 1 Cordelia Schmid 1 Cheng-Lin Liu 2 
* Corresponding author
1 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
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 the 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 eight datasets, namely KTH, YouTube, Hollywood2, UCF sports, IXMAS, UIUC, Olympic Sports and UCF50. On all datasets our approach outperforms current state-of-the-art results.
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Submitted on : Friday, January 25, 2013 - 6:25:42 PM
Last modification on : Thursday, October 27, 2022 - 4:02:50 AM
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  • HAL Id : hal-00725627, version 2


Heng Wang, Alexander Kläser, Cordelia Schmid, Cheng-Lin Liu. Dense trajectories and motion boundary descriptors for action recognition. [Research Report] RR-8050, INRIA. 2012. ⟨hal-00725627v2⟩



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