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Learning Hierarchical Features for Visual Object Tracking with Recursive Neural Networks
[article]
2018
arXiv
pre-print
Recently, deep learning has achieved very promising results in visual object tracking. Deep neural networks in existing tracking methods require a lot of training data to learn a large number of parameters. However, training data is not sufficient for visual object tracking as annotations of a target object are only available in the first frame of a test sequence. In this paper, we propose to learn hierarchical features for visual object tracking by using tree structure based Recursive Neural
arXiv:1801.02021v1
fatcat:7dsoje5c2rdfbcjkg2qtd7fove