目标跟踪中基于IoU和中心点距离预测的尺度估计

李绍明; 储珺; 冷璐; 涂序继

doi:10.16383/j.aas.c210356

目标跟踪中基于IoU和中心点距离预测的尺度估计

doi: 10.16383/j.aas.c210356

李绍明^{1, 2,},
储珺^{1, 2,},
冷璐^{1, 2,},
涂序继^{1, 2,}

1.
南昌航空大学软件学院南昌 330063
2.
江西省图像处理与模式识别重点实验室南昌 330063

基金项目: 国家自然科学基金(62162045), 江西省科技支撑计划项目(20192BBE50073)资助

详细信息

作者简介:
李绍明：南昌航空大学软件学院硕士研究生. 主要研究方向为计算机视觉和目标跟踪. E-mail: thorn_mo1905@163.com

储珺：南昌航空大学软件学院教授. 主要研究方向为计算机视觉和模式识别. 本文通信作者.E-mail: chuj@nchu.edu.cn

冷璐：南昌航空大学软件学院教授. 主要研究方向为图像处理, 生物特征模板保护和生物特征识别. E-mail: leng@nchu.edu.cn

涂序继：南昌航空大学软件学院讲师. 主要研究方向为计算机视觉和图像处理. E-mail: 71068@nchu.edu.cn

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出版历程
- 收稿日期: 2021-04-24
- 录用日期: 2021-11-02
- 网络出版日期: 2021-11-29

Accurate Scale Estimation With IoU and Distance Between Centroids for Object Tracking

LI Shao-Ming^{1, 2
,},
CHU Jun^{1, 2
,},
LENG Lu^{1, 2
,},
TU Xu-Ji^{1, 2
,}

1.
School of Software, Nanchang Hangkong University, Nanchang 330063
2.
Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition, Nanchang Hangkong University, Nanchang 330063

Funds: Supported by National Natural Science Foundation of China (62162045) and Jiangxi Provincial Science and Technology Key Project (20192BBE50073)

More Information

Author Bio:
LI Shao-Ming　Master student at the School of Software, Nanchang Hangkong University. His research interest covers computer vision and object tracking

CHU Jun　Professor at the School of Software, Nanchang Hangkong University. Her research interest covers computer vision and pattern recognition. Corresponding author of this paper

LENG Lu　Professor at the School of Software, Nanchang Hangkong University. His research interest covers image processing, biometric template protection and biometric recognition

TU Xu-Ji　Lecturer at the School of Software, Nanchang Hangkong University. His research interest covers computer vision and image processing

摘要

摘要: 通过分析基于交并比(Intersection over union, IoU)预测的尺度估计模型的梯度更新过程, 发现其在训练和推理过程仅将IoU作为度量, 缺乏对预测框和真实目标框中心点距离的约束, 导致外观模型更新过程中模板受到污染, 前景和背景分类时定位出现偏差. 基于此发现, 构建了一种结合IoU和中心点距离的新度量NDIoU (Normalization distance IoU), 在此基础上提出一种新的尺度估计方法, 并将其嵌入判别式跟踪框架. 即在训练阶段以NDIoU为标签, 设计了具有中心点距离约束的损失函数监督网络的学习, 在线推理期间通过最大化NDIoU微调目标尺度, 以帮助外观模型更新时获得更加准确的样本. 在七个数据集上与相关主流方法进行对比, 所提方法的综合性能优于所有对比算法. 特别是在GOT-10k数据集上, 所提方法的AO、$ S{R}_{0.5} $和$ S{R}_{0.75} $三个指标达到了65.4%、78.7%和53.4%, 分别超过基线模型4.3%、7.0%和4.2%.
- 目标跟踪 /
- 交并比 /
- 尺度估计 /
- 中心点距离
Abstract: This paper first analyzes the gradient update process of the scale estimation model of intersection over union (IoU) prediction in detail, and finds that when the IoU is used as a metric in the training and inference process, the target scale estimation in the tracking process is inaccurate due to the absence of distance between the two centroids. As a result, the template is polluted in the updating process of the object appearance model, which cannot discriminate the target and environment. With this insight, we propose a new metric NDIoU (normalization distance IoU) that combines the IoU and distance between two centroids to estimate the target scale and proposes a new scale estimation method, which is embedded into the discriminative tracking framework. NDIoU is used as the label to supervise the learning of the network and train the scale regression model. During online inference, NDIoU is maximized to fine-tune the target scale. Finally, the proposed method is embedded into the discriminative tracking framework and compared with related state-of-the-art methods on seven data sets. The extensive experiments demonstrate that our method outperforms all the state-of-the-art algorithms. Especially, on the GOT-10k dataset, our method achieves 65.4%, 78.7% and 53.4% on the three metrics of AO, $ S{R}_{0.5} $ and $ S{R}_{0.75} $, which are better than the baseline by 4.3%, 7.0% and 4.2%, respectively.
- Object tracking /
- intersection over union (IoU) /
- scale estimation /
- distance between centroids

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图 1 IoU相同但中心点距离不同的情况(红色代表候选的边界框, 绿色代表真实边界框)

Fig. 1 Same IoU while different distances between centroids (Red represents the candidate bounding box, and green represents the ground-truth box)

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图 2 标准化中心点之间的距离

Fig. 2 Normalized distance between centroids

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图 3 IoU和中心点距离对应视频帧数的统计

Fig. 3 The number statistics of video frame corresponding to IoU and distances between centroids

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图 4 在视频序列Dinosaur上跟踪的结果可视化

Fig. 4 Visualization of tracking results on the video sequence Dinosaur

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图 5 本文方法(ASEID)在OTB-100数据集上与相关方法的比较

Fig. 5 Comparison of the proposed method (ASEID) with related algorithms on OTB-100 dataset

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图 6 OTB-100 数据集不同挑战性因素影响下的成功率图

Fig. 6 Success plots on sequences with different challenging attributes on OTB-100 dataset

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图 7 OTB-100 数据集不同挑战性因素影响下的准确率图

Fig. 7 Precision plots on sequences with different challenging attributes on OTB-100 dataset

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图 8 本文方法与相关方法的可视化比较

Fig. 8 Visualization comparison of the proposed method and related trackers

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图 9 OTB-100数据集中的失败案例(绿色框代表真实框, 红色框代表本文算法的预测框)

Fig. 9 Failure cases in OTB-100 dataset (The green bounding box is ground truth, and the red box represents the prediction box)

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图 10 GOT-10k数据集中的失败案例(在GOT-10k的测试集中, 由于只能拿到测试视频序列的第一帧的真实框, 因此第一帧的标记代表被跟踪目标)

Fig. 10 Failure cases in GOT-10k dataset (In GOT-10k test set, only the ground truth in the first frame of the test dataset can be obtained. Therefore, the ground truth of the first frame represents the tracked target)

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表 1 OTB-100数据集上的消融实验

Table 1 Ablation study on OTB-100 dataset

方法	AUC (%)	Precision (%)	Norm.Pre (%)	帧速率(帧/s)
多尺度搜索	68.4	88.8	83.8	21
IoU	68.4	89.4	84.2	35
NDIoU	69.8	91.3	87.3	35

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表 2 在UAV123数据集上和SOTA算法的比较(%)

Table 2 Compare with SOTA trackers on UAV123 dataset (%)

	SiamBAN^[33]	CGACD^[34]	POST^[35]	MetaRTT^[36]	ECO^[37]	UPDT^[38]	DaSiamRPN^[39]	ATOM^[7]	DiMP50 (基线)^[14]	ASEID (本文)
AUC	63.1	63.3	62.9	56.9	52.4	54.2	56.9	63.2	64.3	64.5
Precision	83.3	83.3	80.0	80.9	74.1	76.8	78.1	84.4	85.0	86.1
Norm.Pre	—	—	—	—	66.8	70.9	74.2	79.1	80.5	81.6

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表 3 在VOT2018数据集上与SOTA方法的比较

Table 3 Compare with SOTA trackers on VOT2018 dataset

	DRT^[40]	RCO^[22]	UPDT^[38]	DaSiamRPN^[39]	MFT^[41]	LADCF^[42]	ATOM^[9]	SiamRPN++^[16]	Dimp50 (基线)^[14]	PrDiMP50^[15]	ASEID (本文)
EAO	0.356	0.376	0.378	0.383	0.385	0.389	0.401	0.414	0.440	0.442	0.454
Robustness	0.201	0.155	0.184	0.276	0.140	0.159	0.204	0.234	0.153	0.165	0.153
Accuracy	0.519	0.507	0.536	0.586	0.505	0.503	0.590	0.600	0.597	0.618	0.615

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表 4 在GOT-10k数据集上与SOTA方法的比较(%)

Table 4 Compare with SOTA trackers on GOT-10k dataset (%)

	DCFST^[30]	PrDiMP50^[15]	KYS^[17]	SiamFC++^[13]	D3S^[43]	Ocean^[12]	ROAM^[44]	ATOM^[7]	DiMP50 (基线)^[14]	ASEID (本文)
$ \mathit{S}{\mathit{R}}_{0.50}$	68.3	73.8	75.1	69.5	67.6	72.1	46.6	63.4	71.7	78.7
$ \mathit{S}{\mathit{R}}_{0.75} $	44.8	54.3	51.5	47.9	46.2	—	16.4	40.2	49.2	53.4
$ \mathit{A}\mathit{O}$	59.2	63.4	63.6	59.5	59.7	61.1	43.6	55.6	61.1	65.4

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表 5 在LaSOT数据集上与SOTA方法的比较(%)

Table 5 Compare with SOTA trackers on LaSOT dataset (%)

	ASRCF^[6]	POST^[35]	Ocean^[12]	GlobalT^[45]	SiamRPN++^[19]	ROAM^[44]	ATOM^[9]	DiMP50 (基线)^[14]	ASEID (本文)
Precision	33.7	46.3	56.6	52.7	56.9	44.5	50.5	56.9	57.5
Success (AUC)	35.9	48.1	56.0	52.1	49.6	44.7	51.4	56.9	57.2

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表 6 在TrackingNet上与SOTA方法的比较(%)

Table 6 Compare with SOTA trackers on TrackingNet (%)

	MDNet^[46]	ECO^[37]	DaSiamRPN^[39]	D3S^[43]	ROAM^[44]	CGACD^[34]	ATOM^[9]	Dimp50 (基线)^[14]	ASEID (本文)
AUC	60.6	55.4	63.8	72.8	67.0	71.1	70.3	74.0	75.3
Precision	56.5	49.2	59.1	66.4	62.3	69.3	64.8	68.7	71.1
Norm.Pre	70.5	61.8	73.3	—	—	—	77.1	80.1	81.9

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表 7 在TC128上与SOTA算法比较(%)

Table 7 Compare with SOTA trackers on TC128 (%)

	POST^[35]	MetaRTT^[36]	ASRCF^[6]	UDT^[47]	TADT^[29]	Re2EMA^[48]	RTMDNet^[49]	MLT^[50]	DiMP50 (基线)^[14]	ASEID (本文)
AUC	56.3	59.7	60.3	54.1	56.2	52.1	56.3	49.8	61.2	63.2
Precision	78.1	80.0	82.5	71.7	—	69.5	78.8	—	81.0	84.2

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