HCDC-SRCF tracker: Learning an adaptively multi-feature fuse tracker in spatial regularized correlation filters framework

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Integrating multi-feature based on multi-layer features from the convolutional network or based on multiple hand-crafted features has been proved to be an effective way for improving tracking performance. In this work, we investigate how to integrate multi-layer convolutional features with hand-crafted features. Specifically, an adaptive multi-feature fusion strategy is proposed based on convolutional features from ResNet-101 and hand-crafted features from HOG as well as Grayscale in spatial regularized correlation filter framework. We fully consider the complementary advantages of multi-layer convolutional features and hand-crafted features to construct a robust and reliable appearance representation of the target. Comprehensive experimental results on benchmark datasets demonstrate that our tracker has achieved significant performance improvements in various challenging environments. Compared to the trackers based only on multi-layer convolutional features or complete hand-crafted fusion features, the most important is that our proposed tracker obtains more competitive tracking performance. Our tracker is publicly available. You can find open-sourced code of our tracker at https://github.com/binger1225/HCDC-SRCF.