Applied multivariate statistical analysis
Applied multivariate statistical analysis
Machine vision
The nature of statistical learning theory
The nature of statistical learning theory
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human motion analysis: a review
Computer Vision and Image Understanding
Multiple Cues used in Model-Based Human Motion Capture
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Performance Evaluation of Vision-Based Real-Time Motion Capture
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
An Introduction to the Kalman Filter
An Introduction to the Kalman Filter
Silhouette and stereo fusion for 3D object modeling
Computer Vision and Image Understanding - Model-based and image-based 3D scene representation for interactive visalization
International Journal of Computer Vision
Markerless Motion Capture using Multiple Cameras
CVIIE '05 Proceedings of the Computer Vision for Interactive and Intelligent Environment
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
Effective face detection using a small quantity of training data
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
Appearance-based virtual view generation from multicamera videos captured in the 3-D room
IEEE Transactions on Multimedia
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In this paper, we propose a gesture modeling system based on computer vision in order to recognize a gesture naturally without any trouble between a system and a user using real-time 3D modeling information on multiple objects. It recognizes a gesture after 3D modeling and analyzing the information pertaining to the user's body shape in stereo views for human movement. In the 3D-modeling step, 2D information is extracted from each view by using an adaptive color difference detector. Potential objects such as faces, hands, and feet are labeled by using the information from 2D detection. We identify reliable objects by comparing the similarities of the potential objects that are obtained from both the views. We acquire information on 2D tracking from the selected objects by using the Kalman filter and reconstruct it as a 3D gesture. A joint of each part of a body is generated in the combined objects. We experimented on ambiguities using occlusion, clutter, and irregular 3D gestures to analyze the efficiency of the proposed system. In this experiment, the proposed gesture modeling system showed a good detection and a processing time of 30 frames per second, which can be used in a real-time.