Correlation filter tracking method via metric learning and adaptive multi-stage appearance

Yan Hong, Jing Li*, Yafu Xiao, Wenfan Zhang, Chengfang Song, Shan Xue

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

Abstract

In the complex tracking environment such as background clutter, generally there are multiple peaks in the response map of the correlation filter. It is difficult to distinguish the real object from the interference; and using the fixed learning rate to update the appearance model, it is not only difficult to maintain the sample diversity, but also easy to introduce noise information. Aiming at this problem, this paper proposes a correlation filter tracking method via metric learning and adaptive multi-stage appearance. By introducing metric learning to discriminate candidate samples corresponding to multiple peaks in the response map, the influence of multi-peak response map on tracking results in complex environments such as background clutter is eliminated; the Gaussian mixture model is used to divide the object appearance samples into groups and assign corresponding weights according to the duration, and the redundant information is eliminated while maintaining the diversity of the appearance model samples. The experimental results on OTB100 and VOT2017 datasets show that the overall precision score obtained by the algorithm in this paper is 0.866. The overall success plot score is 0.628, and the expected average overlap score is 0.211, which is better than most existing tracking methods.

Original languageEnglish
Title of host publication2019 International Joint Conference on Neural Networks, IJCNN 2019
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1-8
Number of pages8
ISBN (Electronic)9781728119854, 9781728120096
DOIs
Publication statusPublished - 2019
Event2019 International Joint Conference on Neural Networks, IJCNN 2019 - Budapest, Hungary
Duration: 14 Jul 201919 Jul 2019

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2019-July

Conference

Conference2019 International Joint Conference on Neural Networks, IJCNN 2019
CountryHungary
CityBudapest
Period14/07/1919/07/19

Keywords

  • correlation filtering
  • Gaussian mixture model
  • metric learning
  • object tracking

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