Structured Human-Head Pose Representation for Estimation Using Fuzzy Lattice Reasoning (FLR)

2020 ◽  
Author(s):  
Vassilis G. Kaburlasos ◽  
Chris Lytridis ◽  
Christos Bazinas ◽  
George A. Papakostas ◽  
Abdelwahab Naji ◽  
...  
Keyword(s):  
Human Head ◽  
Head Pose ◽  
Fuzzy Lattice

An approach for fast human head pose estimation

2011 ◽  
Author(s):  
Yessenia Yari ◽  
Jacob Scharcanski
Keyword(s):  
Pose Estimation ◽  
Human Head ◽  
Head Pose Estimation ◽  
Head Pose

Phenomenal Competition for Poses of the Human Head

Perception ◽  
1996 ◽  
Vol 25 (3) ◽  
pp. 367-368 ◽  
Author(s):  
Daniel Kersten ◽  
Nikolaus F Troje ◽  
Heinrich H Bülthoff
Keyword(s):  
Human Head ◽  
Head Pose ◽  
Cylindrical Projection

We show a cylindrical projection of the human head. This projection is ambiguous with respect to head pose. Viewing such a projection produces perceptual competition for a few discrete views.

Head Detection by the Adaboost Algorithm for Head Pose Estimation

2013 ◽  
Vol 427-429 ◽  
pp. 1696-1699
Author(s):  
Xiang Yang Liu ◽  
Shao Song Zhu ◽  
Su Qing Wu ◽  
Zhi Wei Shen
Keyword(s):  
Pose Estimation ◽  
Nearest Neighbor ◽  
Human Head ◽  
Head Pose Estimation ◽  
Head Tracking ◽  
Head Pose ◽  
Adaboost Algorithm ◽  
Head Detection ◽  
Dimensionality Reduction Method ◽  
Tracking Module

For human head pose analysis based on videos, pose is usually estimated on the head patch provided by a tracking module. However, head tracking is very sensitive to the large changes of pose. Therefore, this work locates the head patch in the videos by head detection. Firstly, we use the Adaboost algorithm to detect the human head in the video. Secondly, we present a dimensionality reduction method to process the head patch. Finally, we use the nearest neighbor method to estimate the head pose. The experiment results show: accurate head detecting helps to estimate the head pose. This method can be used for complex conditions of accurate head pose estimation.

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