Template Attentional Siamese Network for Object Tracking
Pages 218 - 221
Abstract
Recent years, visual object tracking has attracted more and more attention as a fundamental topic. Many deep based trackers, especially Siamese Network based trackers, have achieved state-of-the-art performance on multiple benchmarks. However, most of these trackers applied with the first frame as template throughout the tracking process. We propose a Template Attentional Siamese Network called TASNet. The core of TASNet is combining the detection results of two template frames, where the first frame extracting discriminative features and the latest frame capturing the motion changes, to enhance model tracking effect. Template-wise weights are calculated from attention mechanism to integrate the detecting results of two templates in current frame tracking. The proposed architecture is trained from end to end on the ILSVRC2015 video dataset. Our tracker operates at frame-rates real-time and achieves state-of-the-art tracking accuracy while large deformation of the object is appeared.
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Index Terms
- Template Attentional Siamese Network for Object Tracking
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Published In
December 2018
252 pages
ISBN:9781450366137
DOI:10.1145/3301506
Copyright © 2018 ACM.
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In-Cooperation
- Kyoto University: Kyoto University
- TU: Tianjin University
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 29 December 2018
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ICVIP 2018
ICVIP 2018: 2018 the 2nd International Conference on Video and Image Processing
December 29 - 31, 2018
Hong Kong, Hong Kong
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