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Recurrent Filter Learning for Visual Tracking
[article]
2017
arXiv
pre-print
Recently using convolutional neural networks (CNNs) has gained popularity in visual tracking, due to its robust feature representation of images. Recent methods perform online tracking by fine-tuning a pre-trained CNN model to the specific target object using stochastic gradient descent (SGD) back-propagation, which is usually time-consuming. In this paper, we propose a recurrent filter generation methods for visual tracking. We directly feed the target's image patch to a recurrent neural
arXiv:1708.03874v1
fatcat:i3fwad3ubzg47lkjvozh3lz5ya