In this work, we propose a novel staged depthwise correlation and feature
fusion network, named DCFFNet, to further optimize the feature extraction for
visual tracking. We build our deep tracker upon a siamese network architecture,
which is offline trained from scratch on multiple large-scale datasets in an
end-to-end manner. The model contains a core component, that is, depthwise
correlation and feature fusion module (correlation-fusion module), which
facilitates model to learn a set of optimal weights for a specific object by
utilizing ensembles of multi-level features from lower and higher layers and
multi-channel semantics on the same layer. We combine the modified ResNet-50
with the proposed correlation-fusion layer to constitute the feature extractor
of our model. In training process, we find the training of model become more
stable, that benifits from the correlation-fusion module. For comprehensive
evaluations of performance, we implement our tracker on the popular benchmarks,
including OTB100, VOT2018 and LaSOT. Extensive experiment results demonstrate
that our proposed method achieves favorably competitive performance against
many leading trackers in terms of accuracy and precision, while satisfying the
real-time requirements of applications.

我们提出了一种名为 DCFFNet 的新型分阶段深度相关性和特征融合网络，用于进一步优化可视化跟踪的特征提取。我们基于孪生网络架构构建了我们的深度跟踪器，该网络从头开始离线训练，利用多个大规模数据集进行端到端训练。我们将改进的 ResNet-50 与提出的相关性融合层相结合，构成我们模型的特征提取器。广泛的实验结果表明，我们提出的方法在准确性和精度方面与许多领先的跟踪器相比具有竞争力，同时满足应用的实时要求。