Multimodal multipart learning for action recognition in depth videos
The articulated and complex nature of human actions makes the task of action recognition difficult. One approach to handle this complexity is dividing it to the kinetics of body parts and analyzing the actions based on these partial descriptors. We propose a joint sparse regression based learning me...
Saved in:
Main Authors: | Shahroudy, Amir, Ng, Tian-Tsong, Yang, Qingxiong, Wang, Gang |
---|---|
Other Authors: | School of Electrical and Electronic Engineering |
Format: | Article |
Language: | English |
Published: |
2018
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/87094 http://hdl.handle.net/10220/45224 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Activity recognition in depth videos
by: Amir Shahroudy
Published: (2016) -
Skeleton-based action recognition using spatio-temporal lstm network with trust gates
by: Liu, Jun, et al.
Published: (2020) -
Learning to share latent tasks for action recognition
by: Zhou, Q., et al.
Published: (2014) -
Localized multiple kernel learning for realistic human action recognition in videos
by: Song, Y., et al.
Published: (2013) -
Semantic cues enhanced multimodality multistream CNN for action recognition
by: Tu, Zhigang, et al.
Published: (2020)