面向反制无人机集群的多目标连续鲁棒跟踪算法

doi:10.7527/S1000-6893.2023.29017

Abstract

Abstract:

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

CLC Number:

V279

Chuanyun WANG, Yang SU, Linlin WANG, Tian WANG, Jingjing WANG, Qian GAO. Multi-object continuous robust tracking algorithm for anti-UAV swarm[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(7): 329017-329017.

Figures/Tables 13

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名称	性能指标
系统	Ubuntu18.0.4
内存	16 GB
CPU	InterCore i7-9700k
显卡	NVIDIA GeForce RTX 2080Ti
CUDA	CUDA10.0
Python	Python3.6
Pytorch	Pytorch1.10

评价指标	指标含义
MOTA（↑）	多目标跟踪准确度
IDF1（↑）	目标识别准确度
IDS（↓）	目标ID发生切换的次数
FP（↓）	误检总数
FN（↓）	漏检总数
MT（↑）	至少80%的帧数被正确跟踪的轨迹数量或百分比
ML（↓）	低于20%的帧数被正确跟踪的轨迹数量或百分比

模型	FairMOT	CA	ArcFace	BYTE	MOTA（↑）/%	IDF1（↑）/%	FN（↓）	MT（↑）	ML（↓）	Recall（↑）/%
A	√				67.7	73.2	8 342	176	67	77.4
B	√	√			70.3	74.2	7 808	188	54	78.9
C	√	√	√		71.3	74.7	6 689	199	57	81.9
D（本文模型）	√	√	√	√	73.4	76.1	6 306	196	47	82.8

模型	MOTA（↑）/%	IDF1（↑）/%	IDS（↓）	FP（↓）	FN（↓）	MT（↑）	ML（↓）	Recall（↑）/%
CTrackerV1	46.5	50.7	598	3 429	16 283	119	102	57.1
SORT	59.0	67.0	163	2 175	13 236	149	84	65.1
DeepSORT	61.2	70.3	221	4 375	10 145	169	49	73.3
ByteTrack	65.0	76.5	67	2 878	10 331	175	63	72.8
FairMOT （Baseline）	67.7	73.2	590	2 982	8 342	176	67	77.4
UAVS-MOT（本文方法）	73.4	76.1	740	2 701	6 306	196	47	82.8

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Multi-object continuous robust tracking algorithm for anti-UAV swarm

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