Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning in graphical models
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Human Activity Recognition Using Multidimensional Indexing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Sign Language Analysis: A Survey and the Future beyond Lexical Meaning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robot competition using gesture based interface
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Recovering human body configurations: combining segmentation and recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Multiple frame motion inference using belief propagation
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Recognition of human action for game system
AIS'04 Proceedings of the 13th international conference on AI, Simulation, and Planning in High Autonomy Systems
Detection and tracking of face by a walking robot
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
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In this paper, we propose an approach toward body parts representation, localization, and human pose estimation from an image. In the image, the human body parts and a background are represented by a mixture of Gaussians, and the body parts configuration is modeled by a Bayesian network. In this model, state nodes represent pose parameters of an each body part, and arcs represent spatial constraints. The Gaussian mixture distribution is used to model the prior distribution for the body parts and the background as a parametric model. We estimate the human pose through an optimization of the pose parameters using likelihood objective functions. The performance of the proposed approach is illustrated on various single images, and improves the human pose estimation quality.