@inproceedings{d8ed105bc6d341b88e7da4053e41dffa,
title = "Combining deep learning and preference learning for object tracking",
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.",
keywords = "Deep learning, Object tracking, Preference learning",
author = "Shuchao Pang and Coz, {Juan Jose Del} and Zhezhou Yu and Oscar Luaces and Jorge Diez",
year = "2016",
doi = "10.1007/978-3-319-46675-0_8",
language = "English",
isbn = "9783319466743",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer, Springer Nature",
pages = "70--77",
editor = "Akira Hirose and Kazushi Ikeda and Seiichi Ozawa and Minho Lee and Kenji Doya and Derong Liu",
booktitle = "Neural Information Processing",
address = "United States",
note = "23rd International Conference on Neural Information Processing, ICONIP 2016 ; Conference date: 16-10-2016 Through 21-10-2016",
}