Visual Saliency Based Object Tracking

  • Geng Zhang
  • Zejian Yuan
  • Nanning Zheng
  • Xingdong Sheng
  • Tie Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5995)


This paper presents a novel method of on-line object tracking with the static and motion saliency features extracted from the video frames locally, regionally and globally. When detecting the salient object, the saliency features are effectively combined in Conditional Random Field (CRF). Then Particle Filter is used when tracking the detected object. Like the attention shifting mechanism of human vision, when the object being tracked disappears, our tracking algorithm can change its target to other object automatically even without re-detection. And different from many other existing tracking methods, our algorithm has little dependence on the surface appearance of the object, so it can detect any category of objects as long as they are salient, and the tracking is robust to the change of global illumination and object shape. Experiments on video clips of various objects show the reliable results of our algorithm.


Gaussian Mixture Model Object Tracking Salient Object Conditional Random Field Visual Saliency 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Geng Zhang
    • 1
  • Zejian Yuan
    • 1
  • Nanning Zheng
    • 1
  • Xingdong Sheng
    • 1
  • Tie Liu
    • 2
  1. 1.Institution of Artificial Intelligence and RoboticsXi’an Jiaotong UniversityChina
  2. 2.IBM China Research Lab 

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