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2017 | OriginalPaper | Buchkapitel

TCCF: Tracking Based on Convolutional Neural Network and Correlation Filters

verfasst von : Qiankun Liu, Bin Liu, Nenghai Yu

Erschienen in: Image and Graphics

Verlag: Springer International Publishing

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Abstract

With the rapid development of deep learning in recent years, lots of trackers based on deep learning were proposed, and achieved great improvements compared with traditional methods. However, due to the scarcity of training samples, fine-tuning pre-trained deep models can be easily over-fitted and its cost is expensive. In this paper, we propose a novel algorithm for online visual object tracking which is divided into two separate parts, one of them is target location estimation and the other is target scale estimation. Both of them are implemented with correlation filters independently while using different feature representations. Instead of fine-tuning pre-trained deep models, we update correlation filters. And we design the desired output of correlation filters for every training sample which makes our tracker perform better. Extensive experiments are conducted on the OTB-15 benchmark, and the results demonstrate that our algorithm outperforms the state-of-the-art by great margin in terms of accuracy and robustness.

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Vorheriges Kapitel Deep Scale Feature for Visual Tracking

Nächstes Kapitel PPEDNet: Pyramid Pooling Encoder-Decoder Network for Real-Time Semantic Segmentation

The HOG feature map is visualized with the aid of Pitor’s Computer Vision Matlab Toolbox: https://pdollar.github.io/toolbox/.

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Titel: TCCF: Tracking Based on Convolutional Neural Network and Correlation Filters
verfasst von: Qiankun Liu
Bin Liu
Nenghai Yu
Verlag: Springer International Publishing
Buch: Image and Graphics
Print ISBN: 978-3-319-71606-0

Electronic ISBN: 978-3-319-71607-7

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-71607-7_28

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