Advertisement
Published in:
2016 | OriginalPaper | Chapter
Combining Deep Learning and Preference Learning for Object Tracking
Authors : Shuchao Pang, Juan José del Coz, Zhezhou Yu, Oscar Luaces, Jorge Díez
Published in: Neural Information Processing
Publisher: Springer International Publishing
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
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 \( DPL^{2} \)(Deep & Preference Learning), are competitive with respect to the state-of-the-art algorithms.