Spatial and Temporal Segmented Dense Trajectories for Gesture Recognition

摘要

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.