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Action Recognition by Dense Trajectories (2011)
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BibTeX
@MISC{Wang11actionrecognition,
author = {Heng Wang and Alexander Kläser and Cordelia Schmid and Liu Cheng-lin},
title = {Action Recognition by Dense Trajectories},
year = {2011}
}
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Abstract
Feature trajectories have shown to be efficient for rep-resenting videos. Typically, they are extracted using the KLT tracker or matching SIFT descriptors between frames. However, the quality as well as quantity of these trajecto-ries 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 sam-ple 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 trajec-tories are robust to fast irregular motions as well as shot boundaries. Additionally, dense trajectories cover the mo-tion 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 uncon-trolled realistic videos. We evaluate our video description in the context of action classification with a bag-of-features approach. Experimental results show a significant improve-ment over the state of the art on four datasets of varying difficulty, i.e. KTH, YouTube, Hollywood2 and UCF sports.
Keyphrases
dense trajectory action recognition sift descriptor bag-of-features approach video description sam-ple dense point motion boundary histogram uncon-trolled realistic video rep-resenting video image classification irregular motion ucf sport action classification feature trajectory klt tracker dense optical flow field dense sampling trajectory information novel descriptor displacement information mo-tion information shot boundary state-of-the-art optical flow algorithm recent success state-of-the-art descriptor experimental result significant improve-ment
