Gait Sequence Analysis Using Frieze Patterns
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Recognition of Human Actions Using Moment Based Features and Artificial Neural Networks
MMM '04 Proceedings of the 10th International Multimedia Modelling Conference
Recovering 3D Human Pose from Monocular Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
HMM-based Human Action Recognition Using Multiview Image Sequences
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
High-Speed Human Motion Recognition Based on a Motion History Image and an Eigenspace
IEICE - Transactions on Information and Systems
A novel solution for maze traversal problems using artificial neural networks
Computers and Electrical Engineering
Computers and Electrical Engineering
A PSO-based weighting method for linear combination of neural networks
Computers and Electrical Engineering
Kinematic self retargeting: A framework for human pose estimation
Computer Vision and Image Understanding
A method of abnormal habits recognition in intelligent space
Engineering Applications of Artificial Intelligence
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Correct recognition of human action is the basis for home service robots. Human action recognition is a research hotspot unsolved completely at present. This paper presents a new way of human action recognition. RBF (Radial Basis Function) neural networks-based methodology using silhouette as a representative descriptor of human posture to achieve daily human actions recognition is proposed. The silhouette features of an action sequence are transformed to a grayscale image after building an adaptive background model. Then the gray scale image dimension is reduced by the DCTs (Discrete Cosine Transforms) and the radial basis function neural network is employed to recognize human actions. Finally, the experimental results are provided to show the effectiveness of the proposed method.