Paper Info
Title
Learning rich features from objectness estimation for human lying-pose detection.
Abstract
Lying-pose human detection is an active research field of computer vision in recent years. It has a good theoretical significance and furthermore many applications, such as victim detection or home service robot. But the study on lying-pose human detection in low-altitude overlooking images have many unsolved problems owing to multiple poses, arbitrary orientation, in-plane rotation, perspective distortion, and time-consuming. In this paper, the proposed framework of human lying-pose detection is optimization and machine learning algorithms inspired by processes of neurobiology suggest and human vision system to select possible object locations. First, the proposed model effectively utilizes binarized normed gradient features to obtain the objectness rapidly based on the vision saliency. Further, deep-learning techniques based on the convolution neural network are trained for learning rich feature hierarchies, in order to obtain the object of lying-pose human from objectness estimation, unlike the classical sliding-window algorithm. Eventually, employed pyramid mean-shift algorithm and rotation-angle recovery method to find position and direction of human lying-pose. The experimental results show that our method is rapid and efficient, and that it achieves state-of-the-art results with our XMULP dataset.
Year
DOI
Venue
2017
10.1007/s00530-016-0518-5
Multimedia Syst.
Keywords
Field
DocType
Human lying-pose detection, Deep model, Objectness estimation, Rich features learning, Saliency detection
Computer vision,Perspective distortion,Machine vision,Salience (neuroscience),Computer science,Convolutional neural network,Lying,Artificial intelligence,Pyramid,Hierarchy,Service robot
Journal
Volume
Issue
ISSN
23
4
1432-1882
Citations 
PageRank 
References 
1
0.34
41
Authors
5
Name
Order
Citations
PageRank
Dao-Xun Xia131.72
Song-zhi Su2618.53
Li-Chuan Geng310.34
Guo-xi Wu410.34
Shaozi Li540354.27