Human action recognition by fast dense trajectories

Abstract

In this paper, we propose the fast dense trajectories algorithm for human action recognition. Dense trajectories are robust to fast irregular motions and outperform other state-of-the-art descriptors such as KLT tracker or SIFT descriptors. However, the use of dense trajectories is time consuming. To improve the efficiency, we extract feature trajectories in the ROI rather than in the whole frames, and we use the temporal pyramids to achieve adaptable mechanism for different action speed. We evaluate the method on the dataset of Huawei/3DLife -- 3D human reconstruction and action recognition Grand Challenge in ACM Multimedia 2013. Experimental results show a significant improvement over the dense trajectories descriptor in real-time, and adaptable to different speed.