OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
The Function Space of an Activity
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A Unified Framework for Gesture Recognition and Spatiotemporal Gesture Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient and robust annotation of motion capture data
Proceedings of the 2009 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
A survey on vision-based human action recognition
Image and Vision Computing
Human activity analysis: A review
ACM Computing Surveys (CSUR)
Embedding-based subsequence matching in time-series databases
ACM Transactions on Database Systems (TODS)
Recognition and segmentation of 3-d human action using HMM and multi-class adaboost
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Slow Feature Analysis for Human Action Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Continuous body and hand gesture recognition for natural human-computer interaction
ACM Transactions on Interactive Intelligent Systems (TiiS) - Special Issue on Affective Interaction in Natural Environments
Part-based motion descriptor image for human action recognition
Pattern Recognition
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
Tag localization with spatial correlations and joint group sparsity
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Instructing people for training gestural interactive systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Learning Sparse Representations for Human Action Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Recognition of Human Activities: A Survey
IEEE Transactions on Circuits and Systems for Video Technology
Mining actionlet ensemble for action recognition with depth cameras
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Action recognition by exploring data distribution and feature correlation
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Human activity prediction: Early recognition of ongoing activities from streaming videos
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Action recognition for human-marionette interaction
Proceedings of the 20th ACM international conference on Multimedia
Knowledge adaptation for ad hoc multimedia event detection with few exemplars
Proceedings of the 20th ACM international conference on Multimedia
Kernelized temporal cut for online temporal segmentation and recognition
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Exploring the Trade-off Between Accuracy and Observational Latency in Action Recognition
International Journal of Computer Vision
A survey of video datasets for human action and activity recognition
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data centric research at the University of Queensland
ACM SIGMOD Record
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Online human gesture recognition has a wide range of applications in computer vision, especially in human-computer interaction applications. Recent introduction of cost-effective depth cameras brings on a new trend of research on body-movement gesture recognition. However, there are two major challenges: i) how to continuously recognize gestures from unsegmented streams, and ii) how to differentiate different styles of a same gesture from other types of gestures. In this paper, we solve these two problems with a new effective and efficient feature extraction method that uses a dynamic matching approach to construct a feature vector for each frame and improves sensitivity to the features of different gestures and decreases sensitivity to the features of gestures within the same class. Our comprehensive experiments on MSRC-12 Kinect Gesture and MSR-Action3D datasets have demonstrated a superior performance than the stat-of-the-art approaches.