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Extended Co-occurrence HOG with Dense Trajectories for Fine-Grained Activity Recognition

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Part of the book series:Lecture Notes in Computer Science ((LNIP,volume 9007))

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Abstract

In this paper we propose a novel feature descriptor Extended Co-occurrence HOG (ECoHOG) and integrate it with dense point trajectories demonstrating its usefulness in fine grained activity recognition. This feature is inspired by original Co-occurrence HOG (CoHOG) that is based on histograms of occurrences of pairs of image gradients in the image. Instead relying only on pure histograms we introduce a sum of gradient magnitudes of co-occurring pairs of image gradients in the image. This results in giving the importance to the object boundaries and straightening the difference between the moving foreground and static background. We also couple ECoHOG with dense point trajectories extracted using optical flow from video sequences and demonstrate that they are extremely well suited for fine grained activity recognition. Using our feature we outperform state of the art methods in this task and provide extensive quantitative evaluation.

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Author information

Authors and Affiliations

  1. The University of Tokyo, Tokyo, Japan

    Hirokatsu Kataoka

  2. Keio University, Minato, Japan

    Hirokatsu Kataoka, Kiyoshi Hashimoto & Yoshimitsu Aoki

  3. National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan

    Kenji Iwata & Yutaka Satoh

  4. Technische Universität München (TUM), Munich, Germany

    Nassir Navab & Slobodan Ilic

Authors
  1. Hirokatsu Kataoka

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  2. Kiyoshi Hashimoto

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  3. Kenji Iwata

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  4. Yutaka Satoh

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  5. Nassir Navab

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  6. Slobodan Ilic

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  7. Yoshimitsu Aoki

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Corresponding author

Correspondence toHirokatsu Kataoka.

Editor information

Editors and Affiliations

  1. Technische Universität München, Garching, Germany

    Daniel Cremers

  2. University of Adelaide, Adelaide, South Australia, Australia

    Ian Reid

  3. Keio University, Yokohama, Kanagawa, Japan

    Hideo Saito

  4. University of California at Merced, Merced, California, USA

    Ming-Hsuan Yang

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© 2015 Springer International Publishing Switzerland

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Kataoka, H.et al. (2015). Extended Co-occurrence HOG with Dense Trajectories for Fine-Grained Activity Recognition. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9007. Springer, Cham. https://doi.org/10.1007/978-3-319-16814-2_22

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