30/11/2020

Rotation Axis Focused Attention Network (RAFA-Net) for Estimating Head Pose

Ardhendu Behera, Zachary Wharton, Pradeep Hewage, Swagat Kumar

Keywords:

Abstract Paper Similar Papers

Abstract: Head pose is a vital indicator of human attention and behavior. Therefore, automatic estimation of head pose from images is key to many real-world applications. In this paper, we propose a novel approach for head pose estimation from a single RGB image. Many existing approaches often predict head poses by localizing facial landmarks and then solve 2D to 3D correspondence problem with a mean head model. Such approaches completely rely on the landmark detection accuracy, an ad-hoc alignment step, and the extraneous head model. To address this drawback, we present an end-to-end deep network, which explores rotation axis (yaw, pitch, and roll) focused innovative attention mechanism to capture the subtle changes in images. The mechanism uses attentional spatial pooling from a self-attention layer and learns the importance over fine-grained to coarse spatial structures and combine them to capture rich semantic information concerning a given rotation axis. The experimental evaluation of our approach using three benchmark datasets is very competitive to state-of-the-art methods, including with and without landmark-based approaches.

The video of this talk cannot be embedded. You can watch it here:
https://accv2020.github.io/miniconf/poster_458.html
(Link will open in new window)
 0
 0
 0
 0
  Share    
This is an embedded video. Talk and the respective paper are published at ACCV 2020 virtual conference. If you are one of the authors of the paper and want to manage your upload, see the question "My papertalk has been externally embedded..." in the FAQ section.

Comments

Post Comment
no comments yet
code of conduct: tbd Characters remaining: 140

Similar Papers

 0
 0
 0
 0
 4:34 
 0
 0
 0
 0
 3:20 
 0
 0
 0
 0
 5:03 
 0
 0
 0
 0
 2:50 
 0
 0
 0
 0
 3:01 
 0
 0
 0
 0
 5:30 
 0
 0
 0
 0
 14:39 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 1:00 
 0
 0
 0
 0
 1:00 
 0
 0
 0
 0
 3:02 
 0
 0
 0
 0
 4:57 
 0
 0
 0
 0
 5:01 
 0
 0
 0
 0
 1:00 
 0
 0
 0
 0
 15:17 
 0
 0
 0
 0
 2:55 
 0
 0
 0
 0
 15:58 
 0
 0
 0
 0
 9:58 
 0
 0
 0
 0
 4:55 
 0
 0
 0
 0
 5:02 
 0
 0
 0
 0
 10:56 
 0
 0
 0
 0
 2:55 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 8:46 
 0
 0
 0
 0
 4:34 
 0
 0
 0
 0
 3:19 
 0
 0
 0
 0
 9:49 
 0
 0
 0
 0
 5:36 
 0
 0
 0
 0
 3:25 
 0
 0
 0
 0
 5:17 
 0
 0
 1
 1
 5:08 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 0:59 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 2:49 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 4:33 
 0
 0
 0
 0
 9:47 
 0
 0
 0
 0
 9:50 
 0
 0
 0
 0
 16:24