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
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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
Hou Junhui
中科院分区:
计算机科学2区
文献类型:
--
作者:
Pan Gengzheng;Chen Guochun;Kang Wenxiong;Hou Junhui

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基于相关滤波器的跟踪器在目标跟踪中表现出良好的性能。然而,现有的基于CF的跟踪器的精度和效率都是有限的。在本文中,我们提出了一个强大的和实时的对象跟踪框架,基于一个典型的CF跟踪器。具体来说,我们首先提出了一个自适应的模型更新策略,以防止被污染的跟踪器时,目标被遮挡或消失在视线中。然后,我们提出了一个多模态验证方法,以减少跟踪失败,这是能够自适应地产生潜在的候选人,并评估他们与连体网络。此外,我们建立了一个在线模板库,以增加就业的连体网络的辨别力。在OTB-13和OTB-15基准数据集上的实验结果表明,我们的方法优于最先进的方法。特别是,在OTB-15上,我们的方法不仅在AUC得分上实现了12.3%的相对增益,而且还以高跟踪速度运行,即,58.3帧每秒,与基线CF跟踪器相比。
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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