高速公路广告牌巡检目标跟踪的改进ByteTrack算法

doi:10.11947/j.AGCS.2025.20250149

测绘学报 ›› 2025, Vol. 54 ›› Issue (11): 2068-2080.doi: 10.11947/j.AGCS.2025.20250149

• 地图学与地理信息 • 上一篇

高速公路广告牌巡检目标跟踪的改进ByteTrack算法

李俊¹^,²(), 李朝奎¹(), 黄磊³, 冯媛媛¹^,²

^1.湖南科技大学测绘遥感信息工程湖南省重点实验室，湖南　湘潭　411201
^2.湖南科技大学地球科学与空间信息工程学院，湖南　湘潭　411201
^3.湖南省第一测绘院，湖南　长沙　410004

收稿日期:2025-04-07 修回日期:2025-11-05 发布日期:2025-12-15
通讯作者: 李朝奎 E-mail:1576747472@qq.com;616059644@qq.com
作者简介:李俊（2000—），男，硕士生，研究方向为地图制图学与地理信息工程。E-mail：1576747472@qq.com
基金资助:
国家自然科学基金(42171418);实景三维建设与应用技术湖南省工程研究中心开放课题(3DRS2024H3);实景三维地理环境安徽省重点实验室开放课题(2024PGE001);湖南省自然科学基金(2024JJ8328)

Highway billboard inspection object tracking based on improved ByteTrack algorithm

Jun LI¹^,²(), Chaokui LI¹(), Lei HUANG³, Yuanyuan FENG¹^,²

^1.Hunan Key Laboratory of Surveying, Mapping and Remote Sensing Information Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
^2.College of Geoscience and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
^3.The First Surveying and Mapping Institute of Hunan Province, Changsha 410004, China

Received:2025-04-07 Revised:2025-11-05 Published:2025-12-15
Contact: Chaokui LI E-mail:1576747472@qq.com;616059644@qq.com
About author:LI Jun (2000—), male, postgraduate, majors in cartography and geographic information engineering.　E-mail: 1576747472@qq.com
Supported by:
The National Natural Science Foundation of China(42171418);Real 3D Construction and Application Technology Hunan Engineering Research Center Open Project(3DRS2024H3);Open Project of Anhui Provincial Key Laboratory of Real 3D Geographic Environment(2024PGE001);Hunan Provincial Natural Science Foundation Project(2024JJ8328)

摘要/Abstract

摘要：

采用ByteTrack算法对高速公路巡检车摄像头捕捉到的广告牌进行跟踪，能够提取广告牌视频画面、出现时间节点信息。然而该算法面临着遮挡问题及误跟踪非广告牌目标的挑战，为此，对ByteTrack算法作出以下改进研究。首先，在目标被标识为跟踪ID前需创建缓冲轨迹，直至此轨迹满足预激活判定条件，对处于丢失状态的目标轨迹判断遮挡状态，当预激活目标与遮挡目标符合类别、外观及方位向量等条件时，进行两目标之间的匈牙利匹配；然后，参考Botsort、ByteTrack算法中卡尔曼滤波参数设置特点，使用遗传算法分别对XYAH、XYWH编码方式下卡尔曼滤波关键参数进行调节，对比选择预测效果最佳的卡尔曼滤波。本文以长株潭城市群部分高速公路为试验对象，研究结果表明，相较于原始ByteTrack算法，本文方法的Hota、Mota、IDF指标分别提高了1.318、11.682、2.033个百分比；对比其他的多目标跟踪算法，改进的ByteTrack算法除了FP值略高于Ocsort算法，其他各个指标都优于Botsort、Deepocsort、Hybridsort等算法。改进的ByteTrack算法实现了高速公路广告牌目标的良好跟踪，为高速公路广告牌智能巡检技术提供了参考。

关键词: 高速公路, 巡检车, 广告牌, ByteTrack, 多目标跟踪

Abstract:

The ByteTrack algorithm is used to track the billboards captured by the camera of the highway inspection vehicle, which can extract the video screen of the billboard and the information of the time node. However, the algorithm faces the challenge of occlusion and false tracking of non-advertisement objects. Therefore, the following improvements are made to the ByteTrack algorithm. Firstly, a buffer trajectory needs to be created before the object is identified as the tracking ID until the trajectory satisfies the pre-activation criterion. The occlusion state is judged for the object trajectory in the lost state. When the pre-activated object and the occluded object meet the category, appearance and orientation vector conditions, the Hungarian matching between the two objects is performed. Secondly, referring to the characteristics of Kalman filter parameter setting in Botsort and ByteTrack algorithms, genetic algorithm is used to adjust the key parameters of Kalman filter under XYAH and XYWH coding modes respectively, and the Kalman filter with the best prediction effect is selected. Some expressways in Changsha-Zhuzhou-Xiangtan urban agglomeration are selected as experimental objects. The results show that compared with the original ByteTrack algorithm, the proposed method improves the Hota, Mota and IDF indexes by 1.318%, 11.682% and 2.033%, respectively. Compared with other multi-object tracking algorithms, the improved ByteTrack algorithm is superior to Botsort, Deepocsort, Hybridsort and other algorithms except that the FP value is slightly higher than the Ocsort algorithm. The improved ByteTrack algorithm achieves good tracking of object on highway billboards, which provides a reference for the intelligent inspection technology of highway billboards.

Key words: highway, inspection vehicle, billboard, ByteTrack, multi-object tracking

中图分类号:

P208

李俊, 李朝奎, 黄磊, 冯媛媛. 高速公路广告牌巡检目标跟踪的改进ByteTrack算法[J]. 测绘学报, 2025, 54(11): 2068-2080.

Jun LI, Chaokui LI, Lei HUANG, Yuanyuan FENG. Highway billboard inspection object tracking based on improved ByteTrack algorithm[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(11): 2068-2080.

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区域	判定方法
左边缘	x_tl<E_W
右边缘	x_br>W-E_W
上边缘	y_tl>H-E_H

边界线	交点
x=0	（0，b）
x=W	（W，m×W+b）
y=0
y=H

编码方式	关键参数	表示方式
XYWH	x₀	[x，y，w，h，0，0，0，0]
	P₀	diag[（w/k₁）²，（h/k₁）²，（w/k₁）²，（h/k₁）²，（w/k₂）²，（h/k₂）²，（w/k₂）²，（h/k₂）²]
	Q	diag[（w/k₃）²，（h/k₃）²，（w/k₃）²，（h/k₃）²，（w/k₄）²，（h/k₄）²，（w/k₄）²，（h/k₄）²]
	R	diag[（w/k₅）²，（h/k₅）²，（w/k₅）²，（h/k₅）²]
XYAH	x₀	[x，y，a，h，0，0，0，0]
	P₀	diag[（h/k₁）²，（h/k₁）²，（0.01）²，（h/k₁）²，（h/k₂）²，（h/k₂）²，（0.000 01）²，（h/k₂）²]
	Q	diag[（h/k₃）²，（h/k₃）²，（0.01）²，（h/k₃）²，（h/k₄）²，（h/k₄）²，（0.000 01）²，（h/k₄）²]
	R	diag[（h/k₅）²，（h/k₅）²，（h/k₅）²，（h/k₅）²]

累计跟踪帧数	Hota/（%）↑	Mota/（%）↑	IDF/（%）↑	IDS↓
10	54.058	54.516	77.981	13
20	54.008	56.291	78.183	11
30	53.92	57.208	78.218	10
40	53.396	57.03	77.778	8
50	53.099	56.829	77.316	5
60	52.566	56.173	76.534	4

丢失帧数	Hota/（%）↑	Mota/（%）↑	IDF/（%）↑	IDS↓
30	53.92	57.208	78.218	10
60	53.959	57.143	78.364	5
90	53.963	57.231	78.514	3
120	53.923	57.211	78.506	3
150	53.923	57.211	78.506	3

高速公路广告牌巡检目标跟踪的改进ByteTrack算法

Highway billboard inspection object tracking based on improved ByteTrack algorithm

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参考文献 25

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Metrics

本文评价

改进组合	Hota/%↑	Mota/（%）↑	IDF/（%）↑	FN↓	FP↓	IDS↓
Base（ByteTrack）	44.546	14.055	64.62	4238	13 139	4
Base+GAKF	45.169	13.722	64.446	4250	13 246	4
Base+预激活匹配遮挡	45.146	25.195	66.529	5159	9882	0
Base+GaKF+预激活匹配遮挡	45.864	25.737	66.653	5184	9748	0

多目标跟踪算法	Hota/（%）↑	Mota/（%）↑	IDF/（%）↑	FN↓	FP↓	IDS↓
本文方法	45.864	25.737	66.653	5184	9748	0
Botsort（ReID主干网络为ResNet18）	40.344	15.562	54.089	8708	10 643	129
Deepocsort（ReID主干网络为ResNet18）	42.287	18.988	58.297	7753	9922	42
Ocsort	43.046	18.77	59.65	7452	9669	14
Hybridsort	30.278	17.73	37.779	12 046	14 507	1