Correlation filter tracker with siamese: A robust and real-time object tracking framework
Correlation filter tracker with siamese: A robust and real-time object tracking framework
复制标题
使用 siamese 的相关滤波器跟踪器:一个强大的实时对象跟踪框架
DOI:
10.1016/j.neucom.2019.05.033
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发表时间:
2019-09
期刊:
影响因子:
6
通讯作者:
Hou Junhui
中科院分区:
文献类型:
--
作者:
Pan Gengzheng;Chen Guochun;Kang Wenxiong;Hou Junhui
Correlation filter (CF) based trackers have shown promising performance in object tracking. However, both the accuracy and efficiency of existing CF based trackers are limited. In this paper, we propose a robust and real-time object tracking framework, based on a canonical CF tracker. Specifically, we first propose an adaptive model update strategy for preventing the tracker from being contaminated when the target is occluded or disappears in sight. Then, we propose a multimodal validation method for reducing tracking failures, which is capable of generating potential candidates adaptively and evaluating them with a siamese network. In addition, we build a template library online to augment the discriminability of the employed siamese network. Experimental results over OTB-13 and OTB-15 benchmark datasets demonstrate that our method outperforms state-of-the-art ones. Especially, on OTB-15, our method not only achieves a relative gain of 12.3% in AUC score but also runs at a high tracking speed, i.e., 58.3 frames per second, in comparison with the baseline CF tracker.
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