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Conference Papers Year : 2011

Action Recognition by Dense Trajectories

Alexander Kläser
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  • PersonId : 865348
Cordelia Schmid
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  • PersonId : 831154
Liu Cheng-Lin
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  • PersonId : 836103


Feature trajectories have shown to be efficient for representing videos. Typically, they are extracted using the KLT tracker or matching SIFT descriptors between frames. However, the quality as well as quantity of these trajectories is often not sufficient. Inspired by the recent success of dense sampling in image classification, we propose an approach to describe videos by dense trajectories. We sample dense points from each frame and track them based on displacement information from a dense optical flow field. Given a state-of-the-art optical flow algorithm, our trajectories are robust to fast irregular motions as well as shot boundaries. Additionally, dense trajectories cover the motion information in videos well. We, also, investigate how to design descriptors to encode the trajectory information. We introduce a novel descriptor based on motion boundary histograms, which is robust to camera motion. This descriptor consistently outperforms other state-of-the-art descriptors, in particular in uncontrolled realistic videos. We evaluate our video description in the context of action classification with a bag-of-features approach. Experimental results show a significant improvement over the state of the art on four datasets of varying difficulty, i.e. KTH, YouTube, Hollywood2 and UCF sports.
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inria-00583818 , version 1 (07-04-2011)



Heng Wang, Alexander Kläser, Cordelia Schmid, Liu Cheng-Lin. Action Recognition by Dense Trajectories. CVPR 2011 - IEEE Conference on Computer Vision & Pattern Recognition, Jun 2011, Colorado Springs, United States. pp.3169-3176, ⟨10.1109/CVPR.2011.5995407⟩. ⟨inria-00583818⟩
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