Human Daily Action Analysis with Multi-view and Color-Depth Data

Zhongwei Cheng¹, Lei Qin², Yituo Ye¹, Qingming Huang^{1, 2}, and Qi Tian³

¹Graduate University of Chinese Academy of Sciences, Beijing 100190, China
zwcheng@jdl.ac.cn
ytye@jdl.ac.cn
qmhuang@jdl.ac.cn

²Key Lab of Intelli. Info. Process., ICT CAS, Beijing 100190, China
lqin@jdl.ac.cn

³University of Texas at San Antonio, TX 78249, U.S.A.
qitian@cs.utsa.edu

Abstract. Improving human action recognition in videos is restricted by the inherent limitations of the visual data. In this paper, we take the depth information into consideration and construct a novel dataset of human daily actions. The proposed ACT4² dataset provides synchronized data from 4 views and 2 sources, aiming to facilitate the research of action analysis across multiple views and multiple sources. We also propose a new descriptor of depth information for action representation, which depicts the structural relations of spatiotemporal points within action volume using the distance information in depth data. In experimental validation, our descriptor obtains superior performance to the state-of-the-art action descriptors designed for color information, and more robust to viewpoint variations. The fusion of features from different sources is also discussed, and a simple but efficient method is presented to provide a baseline performance on the proposed dataset.

Keywords: Daily action, Multi-View, RGB-D, Depth descriptor

LNCS 7584, p. 52 ff.

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