@inproceedings{896464666de64a50a4da5133e65fe6c3,
title = "Two-stage learning to robust visual track via CNNs",
abstract = "Convolutional Neural Networks (CNN) are an alternative type of deep neural network that can be used to model local correlations and reduce translation variations, which have demonstrated great performance in some computer vision areas except the visual tracking due to the lack of training data. In this paper, we explore applying a two-stage learning CNN as a generic feature extractor offline pretrained with a large auxiliary dataset and then transfer its rich feature hierarchies to the robust visual tracking task. Instead of traditional neuron models in CNNs, we introduce a strategy to use ReLU for training acceleration. Empirical comparisons prove our CNN based tracker outperforms several state-of-the-art methods on an open tracking benchmark.",
keywords = "Convolutional neural network, Deep learning, Visual tracking",
author = "Dan Hu and Xingshe Zhou and Xiaohao Yu and Zhiqiang Hou",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 8th International Conference on Image and Graphics, ICIG 2015 ; Conference date: 13-08-2015 Through 16-08-2015",
year = "2015",
doi = "10.1007/978-3-319-21969-1_44",
language = "英语",
isbn = "9783319219684",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "491--498",
editor = "Yu-Jin Zhang",
booktitle = "Image and Graphics - 8th International Conference, ICIG 2015, Proceedings",
}