Title: Manifold Warp Segmentation of Human Action
Authors: Liu, SL; Feng, L; Liu, Y; Qiao, H; Wu, J; Wang, W
Author Full Names: Liu, Shenglan; Feng, Lin; Liu, Yang; Qiao, Hong; Wu, Jun; Wang, Wei
Source: IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 29 (5):1414-1426; 10.1109/TNNLS.2017.2672971 MAY 2018
Language: English
Abstract: Human action segmentation is important for human action analysis, which is a highly active research area. Most segmentation methods are based on clustering or numerical descriptors, which are only related to data, and consider no relationship between the data and physical characteristics of human actions. Physical characteristics of human motions are those that can be directly perceived by human beings, such as speed, acceleration, continuity, and so on, which are quite helpful in detecting human motion segment points. We propose a new physical-based descriptor of human action by curvature sequence warp space alignment (CSWSA) approach for sequence segmentation in this paper. Furthermore, time series-warp metric curvature segmentation method is constructed by the proposed descriptor and CSWSA. In our segmentation method, descriptor can express the changes of human actions, and CSWSA is an auxiliary method to give suggestions for segmentation. The experimental results show that our segmentation method is effective in both CMU human motion and video-based data sets.
ISSN: 2162-237X
eISSN: 2162-2388
IDS Number: GD7YK
Unique ID: WOS:000430729100002
PubMed ID: 28287990