Robust visual tracking with correlation filters and metric learning

Abstract: Discriminative correlation filters (DCFs) have been widely used in the visual tracking community in recent years. The DCFs-based trackers determine the target location through a response map generated by the correlation filters and determine the target scale by a fixed scale factor. However, the response map is vulnerable to noise interference and the fixed scale factor also cannot reflect the real scale change of the target, which can obviously reduce the tracking performance. In this paper, to solve the aforementioned drawbacks, we propose to learn a metric learning model in correlation filters framework for visual tracking (called CFML). This model can use a metric learning function to solve the target scale problem. In particular, we adopt a hard negative mining strategy to alleviate the influence of the noise on the response map, which can effectively improve the tracking accuracy. Extensive experimental results demonstrate that the proposed CFML tracker achieves competitive performance compared with the state-of-the-art trackers.

The matlab code for CFML tracker can be downloaded here[google] or here[baidu(password:nvb5)].

1. If you want to compare our results in your experiment, just download the raw experimental results.

2. If you want to test our experiment:

   2.1 Download the code and unzip it in your computer.

   2.2 Run the demo.m to test a tracking sequence using a default model.

If you find the code useful, please cite:

@article{yuan2020robust,
  title={Robust visual tracking with correlation filters and metric learning},
  author={Yuan, Di and Kang, Wei and He, Zhenyu},
  journal={Knowledge-Based Systems},
  pages={https://doi.org/10.1016/j.knosys.2020.105697},
  year={2020},
}

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