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Visual Tracking by Local Superpixel Matching with Markov Random Field

  • Heng Fan
  • Jinhai XiangEmail author
  • Zhongmin Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9916)

Abstract

In this paper, we propose a novel method to track non-rigid and/or articulated objects using superpixel matching and markov random field (MRF). Our algorithm consists of three stages. First, a superpixel dataset is constructed by segmenting training frames into superpixels, and each superpixel is represented by multiple features. The appearance information of target is encoded in the superpixel database. Second, each new frame is segmented into superpixels and then its object-background confidence map is derived by comparing its superpixels with k-nearest neighbors in superpixel dataset. Taking context information into account, we utilize MRF to further improve the accuracy of confidence map. In addition, the local context information is incorporated through a feedback to refine superpixel matching. In the last stage, visual tracking is achieved via finding the best candidate by maximum a posterior estimate based on the confidence map. Experiments show that our method outperforms several state-of-the-art trackers.

Keywords

Visual tracking Superpixel matching Markov Random Field (MRF) Local context information 

Notes

Acknowledgement

This work was primarily supported by Foundation Research Funds for the Central Universities (Program No. 2662016PY008 and Program No. 2662014PY052).

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.College of EngineeringHuazhong Agricultural UniversityWuhanChina
  2. 2.College of InformaticsHuazhong Agricultural UniversityWuhanChina

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