Visual Attention Prediction for Stereoscopic Video by Multi-Module Fully Convolutional Network

You are here

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

Visual Attention Prediction for Stereoscopic Video by Multi-Module Fully Convolutional Network

By: 
Yuming Fang; Chi Zhang; Hanqin Huang; Jianjun Lei

Visual attention is an important mechanism in the human visual system (HVS) and there have been numerous saliency detection algorithms designed for 2D images/video recently. However, the research for fixation detection of stereoscopic video is still limited and challenging due to the complicated depth and motion information. In this paper, we design a novel multi-module fully convolutional network (MM-FCN) for fixation detection of stereoscopic video. Specifically, we design a fully convolutional network for spatial saliency prediction (S-FCN), where the initial spatial saliency map of stereoscopic video is learned by image database of object detection. Furthermore, the fully convolutional network for temporal saliency prediction (T-FCN) is constructed by combining saliency results from S-FCN and motion information from video frames. Finally, the fully convolutional network for depth fixation prediction (D-FCN) is designed to compute the final fixation map of stereoscopic video by learning depth features with spatiotemporal features from T-FCN. The experimental results show that the proposed MM-FCN can predict fixation results for stereoscopic video more effectively and efficiently than other related fixation prediction methods.

SPS on Twitter

  • We are happy to welcome Prof. Jiebo Luo as the new Editor-in-Chief of IEEE Transactions on Multimedia beginning in… https://t.co/9ZgBrgkFXv
  • wants your talents! Our tenure-track position in engineering applications of information and data science a… https://t.co/QrqTAFGlyM
  • If you’re missing out on , don’t worry - we’ll be tweeting all week long. Follow along with us to see the action!

SPS Videos


Signal Processing in Home Assistants

 


Multimedia Forensics


Careers in Signal Processing             

 


Under the Radar