Two-stage learning to robust visual track via CNNs

Dan Hu, Xingshe Zhou, Xiaohao Yu, Zhiqiang Hou

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Convolutional Neural Networks (CNN) are an alternative type of deep neural network that can be used to model local correlations and reduce translation variations, which have demonstrated great performance in some computer vision areas except the visual tracking due to the lack of training data. In this paper, we explore applying a two-stage learning CNN as a generic feature extractor offline pretrained with a large auxiliary dataset and then transfer its rich feature hierarchies to the robust visual tracking task. Instead of traditional neuron models in CNNs, we introduce a strategy to use ReLU for training acceleration. Empirical comparisons prove our CNN based tracker outperforms several state-of-the-art methods on an open tracking benchmark.

Original languageEnglish
Title of host publicationImage and Graphics - 8th International Conference, ICIG 2015, Proceedings
EditorsYu-Jin Zhang
PublisherSpringer Verlag
Pages491-498
Number of pages8
ISBN (Print)9783319219684
DOIs
StatePublished - 2015
Event8th International Conference on Image and Graphics, ICIG 2015 - Tianjin, China
Duration: 13 Aug 201516 Aug 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9219
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th International Conference on Image and Graphics, ICIG 2015
Country/TerritoryChina
CityTianjin
Period13/08/1516/08/15

Keywords

  • Convolutional neural network
  • Deep learning
  • Visual tracking

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