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Spatio-Temporal Context Tracking Algorithm Based on Master-Slave Memory Space Model

  • Xu Li
  • Yong Song
  • Yufei Zhao
  • Yun Li
  • Shangnan Zhao
  • Guowei Shi
  • Xin Yang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 875)

Abstract

The spatio-temporal context (STC) tracking algorithm has the advantages of high tracking accuracy and speed, but it may update the target template incorrectly under complex background and interference conditions. A spatio-temporal context tracking algorithm based on master-slave memory space model is proposed in this paper. The algorithm introduces the memory mechanism of Human Visual System (HVS) into the template updating process of STC algorithm, and forms a memory-based update strategy by constructing the master and slave memory spaces. Meanwhile, a method for determining the target location from multi peak points of saliency is proposed. Experimental results indicate that the proposed algorithm has comparatively high accuracy and robustness in the case of the target under occlusion, attitude changes, the target missing and appearing, and illumination changes, etc.

Keywords

Memory spaces Saliency Spatio-temporal context Tracking algorithm 

Notes

Acknowledgement

This work is supported by National Nature Science Foundation of China (NSFC) (81671787); Defense Industrial Technology Development Program (JCKY2016208B001)

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Xu Li
    • 1
    • 2
  • Yong Song
    • 1
    • 2
  • Yufei Zhao
    • 1
    • 2
  • Yun Li
    • 1
    • 2
  • Shangnan Zhao
    • 1
    • 2
  • Guowei Shi
    • 3
  • Xin Yang
    • 1
    • 2
  1. 1.School of Optics and PhotonicsBeijing Institute of TechnologyBeijingChina
  2. 2.Beijing Key Laboratory for Precision Optoelectronic Measurement Instrument and TechnologyBeijingChina
  3. 3.Institute of Aviation Medicine, AF CPLABeijingChina

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