A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
HMM-Based Action Recognition Using Contour Histograms
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Applying Space State Models in Human Action Recognition: A Comparative Study
AMDO '08 Proceedings of the 5th international conference on Articulated Motion and Deformable Objects
View-Invariant Human Action Detection Using Component-Wise HMM of Body Parts
AMDO '08 Proceedings of the 5th international conference on Articulated Motion and Deformable Objects
Towards Real-Time Human Action Recognition
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
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
Cyclic and non-cyclic gesture spotting and classification in real-time applications
AMDO'10 Proceedings of the 6th international conference on Articulated motion and deformable objects
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Recognizing human action from image sequences is an active area of research in computer vision. In this paper, we present a novel method for human action recognition from image sequences in different viewing angles that uses the Cartesian component of optical flow velocity and human body shape feature vector information. We use principal component analysis to reduce the higher dimensional shape feature space into low dimensional shape feature space. We represent each action using a set of multidimensional discrete hidden Markov model and model each action for any viewing direction. We performed experiments of the proposed method by using KU gesture database. Experimental results based on this database of different actions show that our method is robust.