TY - GEN
T1 - Learning aberrance repressed correlation filters for real-time UAV tracking
AU - Huang, Ziyuan
AU - Fu, Changhong
AU - Li, Yiming
AU - Lin, Fuling
AU - Lu, Peng
N1 - Funding Information:
In this work, aberrance repressed correlation filters have been proposed for UAV visual tracking. By introducing a regularization term to restrict the response map variations to BACF, ARCF is capable of suppressing aberrances that is caused by both background noise information introduced by BACF and appearance changes of the tracked objects. After careful and exhaustive evaluation on three prevalent tracking benchmarks captured by UAVs, ARCF has proved itself to have achieved a big advancement from BACF and have state-of-the-art performance in terms of precision and success rate. Its speed is also more than sufficient for real-time UAV tracking. In conclusion, the proposed method i.e., aberrance repression correlation filters (ARCF), is able to raise the performance of DCF trackers without sacrificing much speed. Out of consideration for computing efficiency due to application of UAV tracking, the proposed ARCF has only used HOG and CN as extracted feature. In cases with low demand for real-time application, more comprehensive features such as convolutional ones can be applied to ARCF for better precision and success rate. Also, the framework of aberrance repression can be extended to other trackers like ECO [5] and SRDCF [7]. We believe, with our proposed aberrance repression method, DCF framework and the performances of DCF based trackers can be further improved. Acknowledgment: This work is supported by the National Natural Science Foundation of China (No. 61806148) and the Fundamental Research Funds for the Central Universities (No. 22120180009).
Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - Traditional framework of discriminative correlation filters (DCF) is often subject to undesired boundary effects. Several approaches to enlarge search regions have been already proposed in the past years to make up for this shortcoming. However, with excessive background information, more background noises are also introduced and the discriminative filter is prone to learn from the ambiance rather than the object. This situation, along with appearance changes of objects caused by full/partial occlusion, illumination variation, and other reasons has made it more likely to have aberrances in the detection process, which could substantially degrade the credibility of its result. Therefore, in this work, a novel approach to repress the aberrances happening during the detection process is proposed, i.e., aberrance repressed correlation filter (ARCF). By enforcing restriction to the rate of alteration in response maps generated in the detection phase, the ARCF tracker can evidently suppress aberrances and is thus more robust and accurate to track objects. Considerable experiments are conducted on different UAV datasets to perform object tracking from an aerial view, i.e., UAV123, UAVDT, and DTB70, with 243 challenging image sequences containing over 90K frames to verify the performance of the ARCF tracker and it has proven itself to have outperformed other 20 state-of-the-art trackers based on DCF and deep-based frameworks with sufficient speed for real-time applications.
AB - Traditional framework of discriminative correlation filters (DCF) is often subject to undesired boundary effects. Several approaches to enlarge search regions have been already proposed in the past years to make up for this shortcoming. However, with excessive background information, more background noises are also introduced and the discriminative filter is prone to learn from the ambiance rather than the object. This situation, along with appearance changes of objects caused by full/partial occlusion, illumination variation, and other reasons has made it more likely to have aberrances in the detection process, which could substantially degrade the credibility of its result. Therefore, in this work, a novel approach to repress the aberrances happening during the detection process is proposed, i.e., aberrance repressed correlation filter (ARCF). By enforcing restriction to the rate of alteration in response maps generated in the detection phase, the ARCF tracker can evidently suppress aberrances and is thus more robust and accurate to track objects. Considerable experiments are conducted on different UAV datasets to perform object tracking from an aerial view, i.e., UAV123, UAVDT, and DTB70, with 243 challenging image sequences containing over 90K frames to verify the performance of the ARCF tracker and it has proven itself to have outperformed other 20 state-of-the-art trackers based on DCF and deep-based frameworks with sufficient speed for real-time applications.
UR - http://www.scopus.com/inward/record.url?scp=85081892400&partnerID=8YFLogxK
U2 - 10.1109/ICCV.2019.00298
DO - 10.1109/ICCV.2019.00298
M3 - Conference article published in proceeding or book
AN - SCOPUS:85081892400
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 2891
EP - 2900
BT - Proceedings - 2019 International Conference on Computer Vision, ICCV 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th IEEE/CVF International Conference on Computer Vision, ICCV 2019
Y2 - 27 October 2019 through 2 November 2019
ER -