Comparison of Classifiers for Human Activity Recognition
IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
Action categorization with modified hidden conditional random field
Pattern Recognition
Real-time multiview recognition of human gestures by distributed image processing
Journal on Image and Video Processing - Special issue on fast and robust methods for multiple-view vision
View-Invariant Human Action Recognition Using Exemplar-Based Hidden Markov Models
ICIRA '09 Proceedings of the 2nd International Conference on Intelligent Robotics and Applications
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
View-independent human action recognition by action hypersphere in nonlinear subspace
PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
Action and gait recognition from recovered 3-D human joints
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
View-invariant gesture recognition using 3D optical flow and harmonic motion context
Computer Vision and Image Understanding
Viewpoint insensitive actions recognition using hidden conditional random fields
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
A survey of vision-based methods for action representation, segmentation and recognition
Computer Vision and Image Understanding
Human action recognition using multiple views: a comparative perspective on recent developments
J-HGBU '11 Proceedings of the 2011 joint ACM workshop on Human gesture and behavior understanding
Human typical action recognition using gray scale image of silhouette sequence
Computers and Electrical Engineering
View invariant action recognition using weighted fundamental ratios
Computer Vision and Image Understanding
Robust human action recognition scheme based on high-level feature fusion
Multimedia Tools and Applications
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In this paper, we present a novel method for human action recognition from any arbitrary view image sequence that uses the Cartesian component of optical flow velocity and human body silhouette feature vector information. We use principal component analysis (PCA) to reduce the higher dimensional silhouette feature space into lower dimensional feature space. The action region in an image frame represents Q-dimensional optical flow feature vector and R-dimensional silhouette feature vector. We represent each action using a set of hidden Markov models and we model each action for any viewing direction by using the combined (Q + R)-dimensional features at any instant of time. We perform experiments of the proposed method by using KU gesture database and manually captured data. Experimental results of different actions from any viewing direction are correctly classified by our method, which indicate the robustness of our view-independent method.