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Open AccessArticle

Fully Convolutional Single-Crop Siamese Networks for Real-Time Visual Object Tracking

Department of IT Convergence Engineering, Kumoh National Institute of Technology, 61 Daehak-ro, Gumi 39177, Gyeongbuk, Korea
Electronics 2019, 8(10), 1084; https://doi.org/10.3390/electronics8101084
Received: 22 August 2019 / Revised: 6 September 2019 / Accepted: 21 September 2019 / Published: 24 September 2019
(This article belongs to the Special Issue Deep Neural Networks and Their Applications)
The visual object tracking problem seeks to track an arbitrary object in a video, and many deep convolutional neural network-based algorithms have achieved significant performance improvements in recent years. However, most of them do not guarantee real-time operation due to the large computation overhead for deep feature extraction. This paper presents a single-crop visual object tracking algorithm based on a fully convolutional Siamese network (SiamFC). The proposed algorithm significantly reduces the computation burden by extracting multiple scale feature maps from a single image crop. Experimental results show that the proposed algorithm demonstrates superior speed performance in comparison with that of SiamFC. View Full-Text
Keywords: visual object tracking; deep learning; convolutional neural networks; Siamese networks visual object tracking; deep learning; convolutional neural networks; Siamese networks
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Lee, D.-H. Fully Convolutional Single-Crop Siamese Networks for Real-Time Visual Object Tracking. Electronics 2019, 8, 1084.

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