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Adversarial Deep Tracking

机译:对抗式深度追踪

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摘要

A number of visual tracking methods achieve the state-of-the-art performance based on deep learning recently. However, most of these trackers utilize the deep neural network in regression task or classification task separately. In this paper, we propose an adversarial deep tracking framework. The framework is composed of a fully convolutional Siamese neural network (regression network) and a discriminative classification network. Then, we jointly optimize the regression network and the classification network by adversarial learning. In the uniform framework, the regression network and classification network can be trained end-to-end as a whole using large amounts of video training data sets. During the testing phase, the regression network generates a response map which reflects the location and the size of the target within each candidate search patch, and the classification network discriminates which response map is the best in terms of the corresponding template patch and candidate search patch. In addition, we propose an attention visualization algorithm for our tracker, and it reflects the area that attracts the attention of our tracker during tracking. The experimental results on three large-scale visual tracking benchmarks (OTB-100, TC-128, and VOT2016) demonstrate the effectiveness of the proposed tracking algorithm and show that our tracker performs comparably against the state-of-the-art trackers.
机译:最近,许多视觉跟踪方法都基于深度学习实现了最先进的性能。但是,大多数这些跟踪器在回归任务或分类任务中分别利用了深度神经网络。在本文中,我们提出了一个对抗式深度跟踪框架。该框架由一个完全卷积的暹罗神经网络(回归网络)和一个判别分类网络组成。然后,我们通过对抗学习共同优化回归网络和分类网络。在统一的框架中,可以使用大量视频训练数据集对回归网络和分类网络进行端到端的整体训练。在测试阶段,回归网络生成一个响应图,该图反映目标在每个候选搜索补丁中的位置和大小,分类网络根据相应的模板补丁和候选搜索补丁来区分哪个响应图是最好的。此外,我们为跟踪器提出了一种注意力可视化算法,该算法反映了在跟踪过程中吸引跟踪器注意的区域。在三个大型视觉跟踪基准(OTB-100,TC-128和VOT2016)上的实验结果证明了所提出的跟踪算法的有效性,并表明我们的跟踪器在性能上与最新的跟踪器相当。

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