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- Publisher Website: 10.1109/IROS45743.2020.9340954
- Scopus: eid_2-s2.0-85102396577
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Conference Paper: Learning Consistency Pursued Correlation Filters for Real-Time UAV Tracking
Title | Learning Consistency Pursued Correlation Filters for Real-Time UAV Tracking |
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Authors | |
Keywords | Correlation Benchmark testing Information filters Unmanned aerial vehicles Real-time systems |
Issue Date | 2020 |
Publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000393 |
Citation | Proceedings of 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA, 24 October - 24 January. 2021, p. 8293-8300 How to Cite? |
Abstract | Correlation filter (CF)-based methods have demonstrated exceptional performance in visual object tracking for unmanned aerial vehicle (UAV) applications, but suffer from the undesirable boundary effect. To solve this issue, spatially regularized correlation filters (SRDCF) proposes the spatial regularization to penalize filter coefficients, thereby significantly improving the tracking performance. However, the temporal information hidden in the response maps is not considered in SRDCF, which limits the discriminative power and the robustness for accurate tracking. This work proposes a novel approach with dynamic consistency pursued correlation filters, i.e., the CPCF tracker. Specifically, through a correlation operation between adjacent response maps, a practical consistency map is generated to represent the consistency level across frames. By minimizing the difference between the practical and the scheduled ideal consistency map, the consistency level is constrained to maintain temporal smoothness, and rich temporal information contained in response maps is introduced. Besides, a dynamic constraint strategy is proposed to further improve the adaptability of the proposed tracker in complex situations. Comprehensive experiments are conducted on three challenging UAV benchmarks, i.e., UAV123@10FPS, UAVDT, and DTB70. Based on the experimental results, the proposed tracker favorably surpasses the other 25 state-of-the-art trackers with real-time running speed (~43FPS) on a single CPU. |
Persistent Identifier | http://hdl.handle.net/10722/304359 |
ISSN | 2023 SCImago Journal Rankings: 1.094 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Fu, C | - |
dc.contributor.author | Yang, X | - |
dc.contributor.author | Li, F | - |
dc.contributor.author | Xu, J | - |
dc.contributor.author | Liu, C | - |
dc.contributor.author | Lu, P | - |
dc.date.accessioned | 2021-09-23T08:58:56Z | - |
dc.date.available | 2021-09-23T08:58:56Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Proceedings of 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA, 24 October - 24 January. 2021, p. 8293-8300 | - |
dc.identifier.issn | 2153-0858 | - |
dc.identifier.uri | http://hdl.handle.net/10722/304359 | - |
dc.description.abstract | Correlation filter (CF)-based methods have demonstrated exceptional performance in visual object tracking for unmanned aerial vehicle (UAV) applications, but suffer from the undesirable boundary effect. To solve this issue, spatially regularized correlation filters (SRDCF) proposes the spatial regularization to penalize filter coefficients, thereby significantly improving the tracking performance. However, the temporal information hidden in the response maps is not considered in SRDCF, which limits the discriminative power and the robustness for accurate tracking. This work proposes a novel approach with dynamic consistency pursued correlation filters, i.e., the CPCF tracker. Specifically, through a correlation operation between adjacent response maps, a practical consistency map is generated to represent the consistency level across frames. By minimizing the difference between the practical and the scheduled ideal consistency map, the consistency level is constrained to maintain temporal smoothness, and rich temporal information contained in response maps is introduced. Besides, a dynamic constraint strategy is proposed to further improve the adaptability of the proposed tracker in complex situations. Comprehensive experiments are conducted on three challenging UAV benchmarks, i.e., UAV123@10FPS, UAVDT, and DTB70. Based on the experimental results, the proposed tracker favorably surpasses the other 25 state-of-the-art trackers with real-time running speed (~43FPS) on a single CPU. | - |
dc.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000393 | - |
dc.relation.ispartof | IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Proceedings | - |
dc.rights | IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Proceedings. Copyright © Institute of Electrical and Electronics Engineers. | - |
dc.rights | ©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | - |
dc.subject | Correlation | - |
dc.subject | Benchmark testing | - |
dc.subject | Information filters | - |
dc.subject | Unmanned aerial vehicles | - |
dc.subject | Real-time systems | - |
dc.title | Learning Consistency Pursued Correlation Filters for Real-Time UAV Tracking | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Lu, P: lupeng@hku.hk | - |
dc.identifier.authority | Lu, P=rp02743 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/IROS45743.2020.9340954 | - |
dc.identifier.scopus | eid_2-s2.0-85102396577 | - |
dc.identifier.hkuros | 325328 | - |
dc.identifier.spage | 8293 | - |
dc.identifier.epage | 8300 | - |
dc.identifier.isi | WOS:000724145802077 | - |
dc.publisher.place | United States | - |