无人机状态检测Kalman滤波空地目标跟踪算法

doi:10.7527/S1000-6893.2024.29834

Abstract

Abstract:

In the UAV’s target tracking process， tracking failure will occur when the target leaves the field of view or is occluded， and similar target interference exists. To overcome these problems， a relocation mechanism based on the tracking status detection and Kalman filter is proposed， which is combined with the fully convolutional Siamese network （siamfc） tracker. In this paper， an air-ground target is taken as the object to be tracked， and UAV is taken as the tracker to track the air-ground target. First， the detection mechanism based on the selection mechanism for double peaks， changing rate of average peak correlation energy， changing rate of maximum response value， and changing rate of peak to sidelobe ratio is used to detect whether the current tracking status is abnormal or not， and determine whether the tracking results of the siamfc satisfy the requirement of being an observed value. Secondly， the Kalman filter utilizes a priori information of the target motion to predict and update the tracking result. When the tracking status is abnormal， it can correct and adjust the tracking result in time. We use the LaSOT video dataset to train the network. Real-time object tracking tests and comparison experiments are conducted on UAV123 aerial dataset and the customized dataset with UAV as the target. The experimental results show that the tracker optimized by the mechanism has an accuracy and success rate of 66.0% and 47.4% （62 frame/s） respectively on UAV123， and 72.0% and 58.6% （55 frame/s） respectively on the customized dataset with UAV as the target. The tracking results meet the real-time requirements of UAV object tracking， and are better than the results of most trackers. The algorithm accomplishes effective target tracking with both UAVs as trackers and tracked objects， and has an enhanced ability to cope with challenging scenarios such as the target’s leaving the field of view， partial or total occlusion of the target， and similar target interference. Meanwhile， the algorithm has good generalization.

Key words: UAV, object tracking, Siamese network, Kalman filter, relocation mechanism, tracking status detection

CLC Number:

V279

Xinyu XU, Jian CHEN. UAV object tracking for air⁃ground targets based on status detection and Kalman filter[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(16): 329834.

Figures/Tables 24

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视频序列	像素大小	长度/帧	场景属性
car4	1 280×720	1 345	SV， ARC， LR， POC， CM， SOB
group1_4	1 280×720	949	SV， ARC， POC， IV， VC， CM， SOB
person10	1 280×720	1 021	SV， FOC， POC， OV， VC， CM， SOB
uav2	720×480	133	SV， LR， FOC， POC

跟踪器	UAV123		与siamfc相比
跟踪器	精确率/%	成功率/%	精确率/%	成功率/%
siamfc	63.7	45.7
siamkal	63.7	45.7	0	0
siamfc_peaks	64.3	46.1	+0.6	+0.4
KPsiamfc	66.0	47.4	+2.3	+1.7

跟踪器	ARC	BC	CM	FM	FOC	IV	LR	OV	POC	SOB	SV	VC
跟踪器	精确率/%
KPsiamfc	57.5	39.9	64.9	54.3	46.7	48.9	54.3	61.7	54.4	63.3	63.0	61.6
siamfc_peaks	55.3	35.7	62.3	56.7	45.8	43.8	53.7	61.4	51.1	60.1	61.4	60.6
siamkal	55.5	37.2	61.8	54.2	43.8	45.7	52.8	60.7	50.5	59.1	60.8	60.8
siamfc	55.2	37.9	61.6	54.9	43.6	45.1	52.3	61.0	50.1	57.7	60.4	60.3
跟踪器	成功率/%
KPsiamfc	40.8	25.8	46.9	38.2	29.2	34.0	34.3	46.1	38.9	45.0	45.1	45.1
siamfc_peaks	39.0	22.9	45.1	39.8	27.9	30.8	34.2	45.0	36.5	42.1	43.9	43.8
siamkal	39.1	23.6	44.8	38.4	26.9	31.7	33.6	44.9	36.0	41.4	43.5	43.9
siamfc	38.9	24.2	44.5	38.1	26.9	31.0	33.3	45.0	35.7	40.7	43.2	43.5

挑战性场景	视频数量	图像数量/帧
光照变化	2	1 290
背景杂波	10	5 145
相似目标干扰	8	5 006
遮挡（部分或全部）	9	5 032
快速移动	11	3 784
小目标	6	2 965
姿态变化明显	7	2 200
离开视野	3	1 244

跟踪器	自制数据集		与siamfc相比
跟踪器	精确率/%	成功率/%	精确率/%	成功率/%
siamfc	67.3	54.7
KPsiamfc	72.0	58.6	+4.7	+3.9

UAV object tracking for air⁃ground targets based on status detection and Kalman filter

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场景属性	siamfc		KPsiamfc		与siamfc相比
场景属性	精确率/%	成功率/%	精确率/%	成功率/%	精确率/%	成功率/%
光照变化	13.6	14.8	14.3	15.9	+0.7	+1.1
背景杂波	58.0	46.8	59.5	48.2	+1.5	+1.4
相似目标干扰	72.5	57.6	81.4	64.5	+8.9	+6.9
遮挡	46.3	34.9	59.7	45.7	+13.4	+10.8
快速移动	66.3	57.8	66.5	58.3	+0.2	+0.5
小目标	66.0	53.8	73.5	59.8	+13.5	+6
姿态变化明显	70.0	62.7	72.4	65.0	+2.4	+2.3
离开视野	50.4	36.0	57.0	42.9	+6.6	+6.9