@inproceedings{625d795bf94744b793da1bfd4374d076,
title = "Extended co-occurrence HOG with dense trajectories for fine-grained activity recognition",
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.",
author = "Hirokatsu Kataoka and Kiyoshi Hashimoto and Kenji Iwata and Yutaka Satoh and Nassir Navab and Slobodan Ilic and Yoshimitsu Aoki",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 12th Asian Conference on Computer Vision, ACCV 2014 ; Conference date: 01-11-2014 Through 05-11-2014",
year = "2015",
doi = "10.1007/978-3-319-16814-2_22",
language = "English",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "336--349",
editor = "Daniel Cremers and Hideo Saito and Ian Reid and Ming-Hsuan Yang",
booktitle = "Computer Vision - ACCV 2014 - 12th Asian Conference on Computer Vision, Revised Selected Papers",
}