A survey of computer vision-based human motion capture
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Recognition of human body motion using phase space constraints
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Human Motion Analysis: A Review
NAM '97 Proceedings of the 1997 IEEE Workshop on Motion of Non-Rigid and Articulated Objects (NAM '97)
International Journal of Computer Vision
Exploring the Space of a Human Action
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
The Function Space of an Activity
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Trace ratio criterion for feature selection
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
A survey on vision-based human action recognition
Image and Vision Computing
Human activity analysis: A review
ACM Computing Surveys (CSUR)
Real-time human pose recognition in parts from single depth images
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
A Multimedia Retrieval Framework Based on Semi-Supervised Ranking and Relevance Feedback
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Multimedia
Online action recognition by template matching
HIS'13 Proceedings of the second international conference on Health Information Science
Hi-index | 0.01 |
Human action recognition is an important area in computer vision and pattern recognition. Human joint position data are regarded as the most effective feature for this task. Depth camera using fringe projection techniques and related software provides us the capability to generate a large amount of human joint position data. However, these data cannot be used as the training data for supervised learning before the action labels are given, and manually labeling all the data is quite time-consuming. In this paper, we propose a novel algorithm named semi-supervised discriminant analysis with global constraint (SDG) which can better estimate the data distribution with both insufficient labeled data and sufficient unlabeled data. We use public mocap dataset HumanEva which is obtained by marker-based motion capture system, and our proposed skeleton dataset captured by depth camera for the evaluation. Experimental results demonstrate the effectiveness of our algorithm.