Deep learning and preference learning for object tracking: a combined approach

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

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


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 DPL2 (Deep and Preference Learning), are competitive with respect to other algorithms.
Original languageEnglish
Pages (from-to)859–876
Number of pages18
JournalNeural Processing Letters
Issue number3
Publication statusPublished - Jun 2018
Externally publishedYes


  • Deep learning
  • Object tracking
  • Preference learning


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