Efficient human parsing based on sketch representation

  • Authors:

  • Meng Wang;Zhaoxiang Zhang;Yunhong Wang

  • Affiliations:

  • Laboratory of Intelligent Recognition and Image Processing, Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, Beihang University, Beijing, China;Laboratory of Intelligent Recognition and Image Processing, Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, Beihang University, Beijing, China;Laboratory of Intelligent Recognition and Image Processing, Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, Beihang University, Beijing, China

  • Venue:
  • ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
  • Year:
  • 2012

Quantified Score

Hi-index 0.00

Visualization

Abstract

In this paper, we present an efficient human parsing method which estimates human body poses from 2D images. Firstly we propose an edge sketch representation, which enhance critical information for pose estimation and prune the redundant. The sketch representation is generated by employing two sets of filters on extracted edges. Based on sketch representation, body part candidates can be located easily using parallel lines detection in Hough space. Then we use specifically trained linear SVM classifiers to detect each body part candidates based on parallel line feature. A dynamic programming algorithm is applied to calculate the MAP estimation based on standard pictorial structure model, which use a kinematic tree to describe human pose. To evaluate the representing ability of proposed sketch representation, as well as the accuracy and efficiency of our entire human pose estimation method, we run two sets of experiments on a sports image dataset respectively. Experimental results demonstrate that the human body parts in the images can be well described by our proposed sketch representation. Furthermore, our human pose estimation method is efficient and achieves comparable accuracy against the state-of-the-art.