TY - GEN
T1 - Combining 3D joints Moving Trend and Geometry property for human action recognition
AU - Liu, Bangli
AU - Yu, Hui
AU - Zhou, Xiaolong
AU - Tang, Dan
AU - Liu, Honghai
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/2/6
Y1 - 2017/2/6
N2 - Depth image based human action recognition has attracted many attentions due to the popularity of the depth sensors. However, accurate recognition still remains a challenge because of various object appearances, poses and video sequences. In this paper, a novel skeleton joints descriptor based on 3D Moving Trend and Geometry (3DMTG) property is proposed for human action recognition. Specifically, a histogram of 3D moving directions between consecutive frames for each joint is constructed to represent the 3D moving trend feature in spatial domain. The geometry information of joints in each frame is modelled by the relative motion with the initial status. The proposed feature descriptor is evaluated on two popular datasets. The experimental results demonstrate the superior performance of our method over the state-of-the-art methods, especially the higher recognition rates for complex actions.
AB - Depth image based human action recognition has attracted many attentions due to the popularity of the depth sensors. However, accurate recognition still remains a challenge because of various object appearances, poses and video sequences. In this paper, a novel skeleton joints descriptor based on 3D Moving Trend and Geometry (3DMTG) property is proposed for human action recognition. Specifically, a histogram of 3D moving directions between consecutive frames for each joint is constructed to represent the 3D moving trend feature in spatial domain. The geometry information of joints in each frame is modelled by the relative motion with the initial status. The proposed feature descriptor is evaluated on two popular datasets. The experimental results demonstrate the superior performance of our method over the state-of-the-art methods, especially the higher recognition rates for complex actions.
KW - 3D Moving Trend
KW - Geometry property
KW - Human action recognition
UR - https://www.scopus.com/pages/publications/85015796431
U2 - 10.1109/SMC.2016.7844262
DO - 10.1109/SMC.2016.7844262
M3 - 会议稿件
AN - SCOPUS:85015796431
T3 - 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings
SP - 332
EP - 337
BT - 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016
Y2 - 9 October 2016 through 12 October 2016
ER -