Staged Depthwise Correlation and Feature Fusion for Siamese Object
  Tracking
Dianbo Ma Jianqiang Xiao Ziyan Gao Satoshi Yamane
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.