The nature of statistical learning theory
The nature of statistical learning theory
Coupled hidden Markov models for complex action recognition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Learning to Detect Objects in Images via a Sparse, Part-Based Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
Behavior recognition via sparse spatio-temporal features
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
A Unified Framework for Gesture Recognition and Spatiotemporal Gesture Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Human Action Segmentation and Recognition Using Discriminative Semi-Markov Models
International Journal of Computer Vision
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
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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As a certain case in the domain of human actions, hand gestures can be expressed by the motion of user's hand to provide nature interaction information in many applications. This paper proposed a hand gesture recognition system based on sparse 3D interest point detector, the spatial-temporal filtering achieved by separable linear Gaussian-Gabor filters detects local motion corners as strong response. The motion trajectory is extracted from the response and different feature formulations are introduced for gesture classification with several solutions. Experimental results demonstrate that the trajectory feature from spatial-temporal (ST) filtering is more reliable than cuboids descriptor, and SVM overcomes KNN and HMM for classification.