Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Machine Learning
Recognition of hand gestures with 3D, nonlinear arm movement
Pattern Recognition Letters
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
One-class svms for document classification
The Journal of Machine Learning Research
Recognition-based gesture spotting in video games
Pattern Recognition Letters
Free viewpoint action recognition using motion history volumes
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
People detection and tracking using stereo vision and color
Image and Vision Computing
Visual recognition of pointing gestures for human-robot interaction
Image and Vision Computing
Three dimensional gesture recognition using modified matching algorithm
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
People detection and tracking through stereo vision for human-robot interaction
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
Identification of humans using gait
IEEE Transactions on Image Processing
| Hi-index | 0.00 |
This paper presents a novel approach for continuous gesture recognition using depth range sensors. Our approach can be seen as an extension of Motion Templates [1] using multiple layers that register the three-dimensional nature of the human gestures. Our Multi-Layered templates are created using depth silhouettes, the extension of binary silhouettes when depth information is available. Both the original Motion Templates and our extension have been tested using several classification approaches in order to determine the best one. These approaches include the use of Hu-moments (originally employed in [1]), PCA and Support Vector Machines. Finally, we propose a methodology for creating a continuous gesture recogniser using motion templates. The methodology is applied both to our representation approach and to the original proposal. In order to validate our proposal, several stereo-video sequences have been recorded showing eight people performing a total of ten different gestures that are prone to be confused when monocular vision is used. The conducted experiments show that our proposal performs a 20% better than the original method.