Spatial and temporal segmented dense trajectories for gesture recognition

Kaho Yamada; Takeshi Yoshida; Kazuhiko Sumi; Hitoshi Habe; Ikuhisa Mitsugami

doi:10.1117/12.2266859

14 May 2017 Spatial and temporal segmented dense trajectories for gesture recognition

Kaho Yamada, Takeshi Yoshida, Kazuhiko Sumi, Hitoshi Habe, Ikuhisa Mitsugami

Proceedings Volume 10338, Thirteenth International Conference on Quality Control by Artificial Vision 2017; 103380F (2017) https://doi.org/10.1117/12.2266859
Event: The International Conference on Quality Control by Artificial Vision 2017, 2017, Tokyo, Japan

Abstract

Recently, dense trajectories [1] have been shown to be a successful video representation for action recognition, and have demonstrated state-of-the-art results with a variety of datasets. However, if we apply these trajectories to gesture recognition, recognizing similar and fine-grained motions is problematic. In this paper, we propose a new method in which dense trajectories are calculated in segmented regions around detected human body parts. Spatial segmentation is achieved by body part detection [2]. Temporal segmentation is performed for a fixed number of video frames. The proposed method removes background video noise and can recognize similar and fine-grained motions. Only a few video datasets are available for gesture classification; therefore, we have constructed a new gesture dataset and evaluated the proposed method using this dataset. The experimental results show that the proposed method outperforms the original dense trajectories.

Citation Download Citation

Kaho Yamada, Takeshi Yoshida, Kazuhiko Sumi, Hitoshi Habe, and Ikuhisa Mitsugami "Spatial and temporal segmented dense trajectories for gesture recognition", Proc. SPIE 10338, Thirteenth International Conference on Quality Control by Artificial Vision 2017, 103380F (14 May 2017); https://doi.org/10.1117/12.2266859

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