Combining deep learning and preference learning for object tracking

Shuchao Pang, Juan Jose Del Coz, Zhezhou Yu, Oscar Luaces, Jorge Diez

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

3 Citations (Scopus)

Abstract

Object tracking is nowadays a hot topic in computer vision. Generally speaking, its aim is to find a target object in every frame of a video sequence. In order to build a tracking system, this paper proposes to combine two different learning frameworks: deep learning and preference learning. On the one hand, deep learning is used to automatically extract latent features for describing the multi-dimensional raw images. Previous research has shown that deep learning has been successfully applied in different computer vision applications. On the other hand, object tracking can be seen as a ranking problem, in the sense that the regions of an image can be ranked according to their level of overlapping with the target object. Preference learning is used to build the ranking function. The experimental results of our method, called DPL2 (Deep & Preference Learning), are competitive with respect to the state-of-the-art algorithms.
Original languageEnglish
Title of host publicationNeural Information Processing
Subtitle of host publication23rd International Conference, ICONIP 2016 Kyoto, Japan, October 16–21, 2016 Proceedings, Part III
EditorsAkira Hirose, Kazushi Ikeda, Seiichi Ozawa, Minho Lee, Kenji Doya, Derong Liu
Place of PublicationCham, Switzerland
PublisherSpringer, Springer Nature
Pages70-77
Number of pages8
ISBN (Print)9783319466743
DOIs
Publication statusPublished - 2016
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9949 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • Deep learning
  • Object tracking
  • Preference learning

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