Human motion analysis: a review
Computer Vision and Image Understanding
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Efficient Visual Event Detection Using Volumetric Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
High-Speed Human Motion Recognition Based on a Motion History Image and an Eigenspace
IEICE - Transactions on Information and Systems
Real-time adaptive hand motion recognition using a sparse bayesian classifier
ICCV'05 Proceedings of the 2005 international conference on Computer Vision in Human-Computer Interaction
Support vector machine to synthesise kernels
Proceedings of the First international conference on Deterministic and Statistical Methods in Machine Learning
Descriptive temporal template features for visual motion recognition
Pattern Recognition Letters
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In this paper, we study the human action classification problem based on motion features directly extracted from video. In order to implement a fast classification system, we select simple features that can be obtained from non-intensive computation. We also introduce the new SVM_2K classifier that can achieve improved performance over a standard SVM by combining two types of motion feature vector together. After learning, classification can be implemented very quickly because SVM_2K is a linear classifier. Experimental results demonstrate the method to be efficient and may be used in real-time human action classification systems.