Extended co-occurrence HOG with dense trajectories for fine-grained activity recognition
Kataoka H, Hashimoto K, Iwata K, Satoh Y, Navab N, Ilic S, Aoki Y (2015)
Publication Type: Conference contribution
Publication year: 2015
Journal
Publisher: Springer Verlag
Book Volume: 9007
Pages Range: 336-349
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Singapore, SGP
ISBN: 9783319168135
DOI: 10.1007/978-3-319-16814-2_22
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.
Involved external institutions
University of Tokyo
Japan (JP)
Keio University / 慶應義塾大学
Japan (JP)
National Institute of Advanced Industrial Science and Technology / 産業技術総合研究所 (AIST)
Japan (JP)
Technische Universität München (TUM)
Germany (DE)
Japan (JP)
Keio University / 慶應義塾大学
Japan (JP)
National Institute of Advanced Industrial Science and Technology / 産業技術総合研究所 (AIST)
Japan (JP)
Technische Universität München (TUM)
Germany (DE)
How to cite
APA:
Kataoka, H., Hashimoto, K., Iwata, K., Satoh, Y., Navab, N., Ilic, S., & Aoki, Y. (2015). Extended co-occurrence HOG with dense trajectories for fine-grained activity recognition. In Daniel Cremers, Hideo Saito, Ian Reid, Ming-Hsuan Yang (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 336-349). Singapore, SGP: Springer Verlag.
MLA:
Kataoka, Hirokatsu, et al. "Extended co-occurrence HOG with dense trajectories for fine-grained activity recognition." Proceedings of the 12th Asian Conference on Computer Vision, ACCV 2014, Singapore, SGP Ed. Daniel Cremers, Hideo Saito, Ian Reid, Ming-Hsuan Yang, Springer Verlag, 2015. 336-349.
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