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Neural Processing Letters

, Volume 47, Issue 3, pp 859–876 | Cite as

Deep Learning and Preference Learning for Object Tracking: A Combined Approach

  • Shuchao Pang
  • Juan José del Coz
  • Zhezhou Yu
  • Oscar Luaces
  • Jorge DíezEmail author
Article
  • 469 Downloads

Abstract

Object tracking is one of the most important processes for object recognition in the field of computer vision. The aim is to find accurately a target object in every frame of a video sequence. In this paper we propose a combination technique of two algorithms well-known among machine learning practitioners. Firstly, we propose a deep learning approach to automatically extract the features that will be used to represent the original images. Deep learning has been successfully applied in different computer vision applications. Secondly, object tracking can be seen as a ranking problem, since the regions of an image can be ranked according to their level of overlapping with the target object (ground truth in each video frame). During object tracking, the target position and size can change, so the algorithms have to propose several candidate regions in which the target can be found. We propose to use a preference learning approach to build a ranking function which will be used to select the bounding box that ranks higher, i.e., that will likely enclose the target object. The experimental results obtained by our method, called \( DPL ^{2}\) (Deep and Preference Learning), are competitive with respect to other algorithms.

Keywords

Deep learning Preference learning Object tracking 

Notes

Acknowledgements

This work was funded by Ministerio de Economía y Competitividad de España (Grant TIN2015-65069-C2-2-R), Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant 20120061110045) and the Project of Science and Technology Development Plan of Jilin Province, China (Grant 20150204007GX). The paper was written while Shuchao Pang was visiting the University of Oviedo at Gijón.

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyJilin UniversityChangchunChina
  2. 2.Artificial Intelligence CenterUniversity of Oviedo at GijónGijónSpain

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