Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion segmentation and pose recognition with motion history gradients
Machine Vision and Applications - Special issue: IEEE WACV
Ghost: A Human Body Part Labeling System Using Silhouettes
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Dance Posture Recognition Using Wide-baseline Orthogonal Stereo Cameras
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Applying 3D human model in a posture recognition system
Pattern Recognition Letters
Human body pose detection using Bayesian spatio-temporal templates
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Recognizing body poses using multilinear analysis and semi-supervised learning
Pattern Recognition Letters
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Human pose estimation and recognition have recently attracted a lot of attention in the field of human-computer interface (HCI) and human-robot interface (HRI). This paper proposes human pose recognition method using a chamfer distance that computes similarities between an input image and pose templates stored in database. However, the chamfer distance has a disadvantage that it may produce false-positive in regions where similar structures in edge images as templates exist, even when no human pose is present. To tackle this problem, the proposed method tries to adaptively attenuate the edges in the background while preserving the edges across foreground/background boundaries and inside the foreground. The proposed algorithm builds on a key observation that edge information in the background is static when a human takes pose as the interface. Moreover, the algorithm additionally considers edge orientation to minimize loss of foreground edges, caused by edge attenuation. In the experiments, the proposed method is applied to the HRI. Edge information for the background is modeled when the robot stops in front of the human for interaction with gesture. The performance of the proposed method, time cost and accuracy, was better than the chamfer distance and pictorial structure method that estimates human pose.