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The Visual Computer

, Volume 33, Issue 9, pp 1103–1119 | Cite as

Multiple cues-based active contours for target contour tracking under sophisticated background

  • Peng LvEmail author
  • Qingjie Zhao
  • Yanming Chen
  • Liujun Zhao
Original Article

Abstract

In this paper, we propose a novel target contour tracking method under sophisticated background using the multiple cues-based active contour model. To locate the target position, a contour-based mean-shift tracker is designed which combines both color and texture information. To reduce the adverse impact of sophisticated background and also accelerate the curve motion, we propose a two-layer-based target appearance model that combines both discriminative pre-learned-based global layer and voting-based local layer. The proposed appearance model is able to extract rough target region from the complex background, which provides important target region information for our active contour model. We subsequently introduce a dynamical shape model to provide prior target shape information for more stable segmentation. To obtain accurate target boundaries, we design a new multiple cues-based active contour model which integrates with target edge, discriminative region, and shape information. The experimental results on 30 video sequences demonstrate that the proposed method outperforms other competitive contour tracking methods under various tracking environment.

Keywords

Object contour tracking Active contours Level sets Segmentation 

Notes

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grand (Nos. 61175096 and 61303245) and Specialized Fund for Joint Building Program of Beijing municipal Education Commission. The authors would also like to thank C. Li, J. Fan, M. Godec, S. Wang, Z. Cai, X. Jia, and M. Yang et al. for providing their source codes for comparisons in our experiments.

Supplementary material

Supplementary material 1 (wmv 15068 KB)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Peng Lv
    • 1
    Email author
  • Qingjie Zhao
    • 1
  • Yanming Chen
    • 1
  • Liujun Zhao
    • 1
  1. 1.Beijing Key Laboratory of Intelligence Information Technology, School of Computer ScienceBeijing Institute of TechnologyBeijingChina

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