BiCF: Learning Bidirectional Incongruity-Aware Correlation Filter for Efficient UAV Object Tracking

Matlab implementation of our Bidirectional Incongruity-Aware Correlation Filters (BiCF) tracker.

Test passed

Publication and citation

This paper has been published by ICRA2020.

You can find this paper here: https://ieeexplore.ieee.org/document/9196530.

Please cite this paper as:

@INPROCEEDINGS{9196530,

author={F. {Lin} and C. {Fu} and Y. {He} and F. {Guo} and Q. {Tang}},

booktitle={2020 IEEE International Conference on Robotics and Automation (ICRA)},

title={BiCF: Learning Bidirectional Incongruity-Aware Correlation Filter for Efficient UAV Object Tracking},

year={2020},

volume={},

number={},

pages={2365-2371},}

Abstract

For more info, please refer to our paper and video.

　　Correlation filters (CFs) have shown excellent performance in unmanned aerial vehicle (UAV) tracking scenarios due to their high computational efficiency. During the UAV tracking process, viewpoint variations are usually accompanied by changes in the object and background appearance, which poses a unique challenge to CF-based trackers. Since the appearance is gradually changing over time, an ideal tracker can not only forward predict the object position but also backtrack to locate its position in the previous frame. There exist response-based errors in the reversibility of the tracking process containing the information on the changes in appearance. However, some existing methods do not consider the forward and backward errors based on while using only the current training sample to learn the filter. For other ones, the applicants of considerable historical training samples impose a computational burden on the UAV. In this work, a novel bidirectional incongruity-aware correlation filter (BiCF) is proposed. By integrating the response-based bidirectional incongruity error into the CF, BiCF can efficiently learn the changes in appearance and suppress the inconsistent error. Extensive experiments on 243 challenging sequences from three UAV datasets (UAV123, UAVDT, and DTB70) are conducted to demonstrate that BiCF favorably outperforms other 25 state-of-the-art trackers and achieves a real-time speed of 45.4 FPS on a single CPU, which can be applied in UAV efficiently.

Quantitative results

UAV123@10fps

DTB70

UAVDT

Getting started

Run demo_BiCF.m script to test the tracker.

Acknowledgements

The feature extraction modules and some of the parameters are borrowed from the ECO tracker (https://github.com/martin-danelljan/ECO).