Visual trackers using deep neural networks have demonstrated favorable performance in object tracking. However, training a deep classification network using overlapped initial target regions may lead an overfitted model. To increase the model generalization, we propose an appearance variation adaptation (AVA) tracker that aligns the feature distributions of target regions over time by learning an adaptation mask in an adversarial network. The proposed adversarial network consists of a generator and a discriminator network that compete with each other over optimizing a discriminator loss in a mini-max optimization problem. Specifically, the discriminator network aims to distinguish recent target regions from earlier ones by minimizing the discriminator loss, while the generator network aims to produce an adaptation mask to maximize the discriminator loss. We incorporate a gradient reverse layer in the adversarial network to solve the aforementioned mini-max optimization in an end-to-end manner. We compare the performance of the proposed AVA tracker with the most recent state-of-the-art trackers by doing extensive experiments on OTB50, OTB100, and VOT2016 tracking benchmarks. Among the compared methods, AVA yields the highest area under curve (AUC) score of 0.712 and the highest average precision score of 0.951 on the OTB50 tracking benchmark. It achieves the second best AUC score of 0.688 and the best precision score of 0.924 on the OTB100 tracking benchmark. AVA also achieves the second best expected average overlap (EAO) score of 0.366, the best failure rate of 0.68, and the second best accuracy of 0.53 on the VOT2016 tracking benchmark.

使用深度神经网络的视觉跟踪器在对象跟踪中表现出良好的性能。但是，使用重叠的初始目标区域训练深度分类网络可能会导致拟合过度。为了提高模型的概括性，我们提出了一种外观变化自适应（AVA）跟踪器，该跟踪器通过学习对抗网络中的自适应掩码来随着时间对准目标区域的特征分布。所提出的对抗网络由生成器和鉴别器网络组成，它们在最小-最大优化问题中彼此竞争以优化鉴别器损耗。具体来说，甄别器网络旨在通过最大程度地减少甄别器损耗，将最近的目标区域与较早的目标区域区分开，而发生器网络旨在产生一个自适应掩码，以使鉴别器损耗最大化。我们在对抗网络中加入了一个反向梯度层，以端到端的方式解决了上述的最小最大优化问题。通过在OTB50，OTB100和VOT2016跟踪基准上进行广泛的实验，我们将建议的AVA跟踪器的性能与最新的跟踪器进行了比较。在比较的方法中，AVA在OTB50跟踪基准上的曲线下面积（AUC）得分最高，为0.712，最高平均精度得分为0.951。在OTB100跟踪基准上，它获得的第二佳AUC得分为0.688，最佳精度得分为0.924。AVA还获得0.366的第二最佳预期平均重叠（EAO）分数，0.68的最佳失败率，