@inproceedings{8665c445ab6b47a1b7c490a9e6903f11,
title = "Study on Deep Learning and Its Application in Visual Tracking",
abstract = "Inspired by recent advances in deep learning, this paper reviews the deep learning methodologies and its applications in object tracking. To overcome the complexity and low-efficiency of existing full-connected deep learning based tracker, we use a novel convolutional deep belief network (CDBN) with convolution, weights sharing and pooling to have much fewer parameters, in addition to gain translation invariance which would benefit the tracker performance. Empirical evaluation demonstrates our CDBN based tracker outperforms several state-of-the-art methods on an open tracker benchmark.",
keywords = "Convolutional deep belief network, deep learning, object tracking",
author = "Dan Hu and Xingshe Zhou and Xiaohao Yu and Zhiqiang Hou",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 10th International Conference on Broadband and Wireless Computing, Communication and Applications, BWCCA 2015 ; Conference date: 04-11-2015 Through 06-11-2015",
year = "2015",
doi = "10.1109/BWCCA.2015.63",
language = "英语",
series = "Proceedings - 2015 10th International Conference on Broadband and Wireless Computing, Communication and Applications, BWCCA 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "240--246",
editor = "Leonard Barolli and Ogiela, \{Marek R.\} and Fatos Xhafa and Lidia Ogiela",
booktitle = "Proceedings - 2015 10th International Conference on Broadband and Wireless Computing, Communication and Applications, BWCCA 2015",
}