ACTA AERONAUTICAET ASTRONAUTICA SINICA >
Multi-object continuous robust tracking algorithm for anti-UAV swarm
Received date: 2023-05-19
Revised date: 2023-07-20
Accepted date: 2023-08-17
Online published: 2023-09-01
Supported by
National Natural Science Foundation of China(61703287);Scientific Research Project of Liaoning Provincial Department of Education(LJKZ0218);Young and middle-aged Science and Technology Innovation Talents Project of Shenyang(RC210401);Doctoral Scientific Research Foundation of Shenyang Aerospace University(22YB03)
Unmanned Aerial Vehicle （UAV） swarm warfare is rapidly developing towards intelligence and practicality， which will pose a huge threat on the future battlefield. Therefore， research on detection and tracking of anti-UAV swarm is imperative. To solve the problems of reduced detection and tracking accuracy caused by mutual occlusion between UAV swarm and weak targets in complex scenes and long-distance detection conditions， this paper proposes a UAVS-Multiple Object Tracking （UAVS-MOT） multi-object continuous robust tracking algorithm. The UAVS-MOT model is based on the multi-branch anchor-free frame prediction structure of the FairMOT model. The coordinate attention module is combined with the DLA-34 network to construct a new backbone feature extraction network， so as to improve the expression ability of feature information. In addition， the new ArcFace Loss function is introduced for training to improve the convergence rate of the model， and the BYTE data association method is used to reduce the target miss rate and improve the track consistency. The experiment shows that the Multiple Object Tracking Accuracy （MOTA） and Identity F1 Score （IDF1） of the proposed algorithm on the UAVSwarm Dataset are 73.4% and 76.1%， respectively， which are 5.7% and 2.9% higher than those of the original FairMOT algorithm. The proposed method can solve the problems of missed and false detection of targets and low tracking accuracy， and has good robustness.
Key words： multiple object tracking; UAV swarm; FairMOT; attention mechanism; anti
Chuanyun WANG , Yang SU , Linlin WANG , Tian WANG , Jingjing WANG , Qian GAO . Multi-object continuous robust tracking algorithm for anti-UAV swarm[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2024 , 45(7) : 329017 -329017 . DOI: 10.7527/S1000-6893.2023.29017
| 1 | 刘雷， 刘大卫， 王晓光， 等. 无人机集群与反无人机集群发展现状及展望［J］. 航空学报， 2022， 43（S1）： 726908. |
| LIU L， LIU D W， WANG X G， et al. Development status and prospect of UAV cluster and anti-UAV cluster［J］. Acta Aeronautica et Astronautica Sinica， 2022， 43（S1）： 726908 （in Chinese）. | |
| 2 | 周末， 孙海文， 王亮， 等. 国外反无人机蜂群作战研究［J］. 指挥控制与仿真， 2023， 45（2）： 24-30. |
| ZHOU M， SUN H W， WANG L， et al. Research on foreign anti-UAV swarm warfare［J］. Command Control & Simulation， 2023， 45（2）： 24-30 （in Chinese）. | |
| 3 | 谢海斌， 闫家鼎， 庄东晔， 等. 无人机集群反制技术剖析［J］. 国防科技， 2021， 42（4）： 10-16. |
| XIE H B， YAN J D， ZHUANG D Y， et al. Analysis of countermeasures against the UAV swarm［J］. National Defense Technology， 2021， 42（4）： 10-16 （in Chinese）. | |
| 4 | 罗俊海， 王芝燕. 无人机探测与对抗技术发展及应用综述［J］. 控制与决策， 2022， 37（3）： 530-544. |
| LUO J H， WANG Z Y. A review of development and application of UAV detection and counter technology［J］. Control and Decision， 2022， 37（3）： 530-544 （in Chinese）. | |
| 5 | 龚轩， 乐孜纯， 王慧， 等. 多目标跟踪中的数据关联技术综述［J］. 计算机科学， 2020， 47（10）： 136-144. |
| GONG X， LE Z C， WANG H， et al. Survey of data association technology in multi-target tracking［J］. Computer Science， 2020， 47（10）： 136-144 （in Chinese）. | |
| 6 | HE Y H， WEI X， HONG X P， et al. Multi-target multi-camera tracking by tracklet-to-target assignment［J］. IEEE Transactions on Image Processing， 2020， 29： 5191-5205. |
| 7 | PENG J L， WANG C G， WAN F B， et al. Chained-tracker： Chaining paired attentive regression results for end-to-end joint multiple-object detection and tracking［C］∥European Conference on Computer Vision. Cham： Springer， 2020： 145-161. |
| 8 | BEWLEY A， GE Z Y， OTT L， et al. Simple online and realtime tracking［C］∥ 2016 IEEE International Conference on Image Processing （ICIP）. Piscataway： IEEE Press， 2016： 3464-3468. |
| 9 | VEERAMANI B， RAYMOND J W， CHANDA P. DeepSort： Deep convolutional networks for sorting haploid maize seeds［J］. BMC Bioinformatics， 2018， 19（S9）： 289. |
| 10 | ZHANG Y F， SUN P Z， JIANG Y， et al. ByteTrack： Multi-object tracking by associating every detection box［C］∥European Conference on Computer Vision. Cham： Springer Cham， 2022： 1-21. |
| 11 | ZHANG Y F， WANG C Y， WANG X G， et al. FairMOT： On the fairness of detection and re-identification in multiple object tracking［J］. International Journal of Computer Vision， 2021， 129（11）： 3069-3087. |
| 12 | HOU Q B， ZHOU D Q， FENG J S. Coordinate attention for efficient mobile network design［C］∥ 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. Piscataway： IEEE Press， 2021： 13708-13717. |
| 13 | DENG J K， GUO J， XUE N N， et al. ArcFace： Additive angular margin loss for deep face recognition［C］∥ 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. Piscataway： IEEE Press， 2019： 4685-4694. |
| 14 | YU F， WANG D Q， SHELHAMER E， et al. Deep layer aggregation［C］∥ 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE Press， 2018： 2403-2412. |
| 15 | LIN T Y， DOLLáR P， GIRSHICK R， et al. Feature pyramid networks for object detection［C］∥ 2017 IEEE Conference on Computer Vision and Pattern Recognition （CVPR）. Piscataway： IEEE Press， 2017： 936-944. |
| 16 | HU J， SHEN L， SUN G. Squeeze-and-excitation networks［C］∥ 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE Press， 2018： 7132-7141. |
| 17 | WOO S， PARK J， LEE J Y， et al. CBAM： Convolutional block attention module［C］∥ European Conference on Computer Vision. Cham： Springer， 2018： 3-19. |
| 18 | ZHOU X Y， KOLTUN V， KR?HENBüHL P. Tracking objects as points［M］∥ VEDALDI A， BISCHOF H， BROX T， et al， ed. Computer Vision-ECCV 2020. Cham： Springer International Publishing， 2020： 474-490. |
| 19 | CIPOLLA R， GAL Y， KENDALL A. Multi-task learning using uncertainty to weigh losses for scene geometry and semantics［C］∥ 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE Press， 2018： 7482-7491. |
| 20 | WANG C Y， SU Y， WANG J J， et al. UAVSwarm dataset： An unmanned aerial vehicle swarm dataset for multiple object tracking［J］. Remote Sensing， 2022， 14（11）： 2601. |
| 21 | LEAL-TAIXé L， MILAN A， REID I， et al. MOTChallenge 2015： Towards a benchmark for multi-target tracking［DB/OL］. arXiv preprint： 1504.01942， 2015. |
| 22 | BERNARDIN K， STIEFELHAGEN R. Evaluating multiple object tracking performance： The CLEAR MOT metrics［J］. Journal on Image and Video Processing， 2008， 2008： 1. |
| 23 | RISTANI E， SOLERA F， ZOU R， et al. Performance measures and a data set for Multi-target， multi-camera tracking［M］∥ Lecture Notes in Computer Science. Cham： Springer International Publishing， 2016： 17-35. |
| 24 | LI Y， HUANG C， NEVATIA R. Learning to associate： Hybrid Boosted multi-target tracker for crowded scene［C］∥ 2009 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE Press， 2009： 2953-2960. |
/
| 〈 |
|
〉 |
CJA