基于深度学习的多目标跟踪关联模型设计

侯建华; 张国帅; 项俊

doi:10.16383/j.aas.c180528

基于深度学习的多目标跟踪关联模型设计

doi: 10.16383/j.aas.c180528

侯建华^1,,
张国帅^1,,
项俊^1, ,

1.
中南民族大学电子信息工程学院武汉 430074

基金项目:

国家自然科学基金 61671484

国家自然科学基金 61701548

湖北省自然科学基金 2018CFB503

中南民族大学中央高校基本科研业务费专项资金项目 CZQ17001

中南民族大学中央高校基本科研业务费专项资金项目 CZZ18001

中南民族大学中央高校基本科研业务费专项资金项目 CZY18046

详细信息

作者简介:
侯建华中南民族大学电子信息工程学院教授. 2007年获华中科技大学模式识别与智能系统博士学位.主要研究方向为计算机视觉与模式识别. E-mail: zil@scuec.edu.cn

张国帅中南民族大学电子信息工程学院硕士研究生. 2016年获长春大学学士学位.主要研究方向为图像处理与模式识别. E-mail: guoshuaiz@scuec.edu.cn

通讯作者:
项俊中南民族大学电子信息工程学院讲师. 2016年获华中科技大学控制科学与工程博士学位.主要研究方向为计算机视觉与模式识别.本文通信作者. E-mail: junxiang@scuec.edu.cn

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出版历程
- 收稿日期: 2018-08-02
- 录用日期: 2019-01-09
- 刊出日期: 2020-12-29

Designing Affinity Model for Multiple Object Tracking Based on Deep Learning

1.
College of Electronic Information Engineering, South-Central University for Nationalities, Wuhan 430074

Funds:

National Natural Science Foundation of China 61671484

National Natural Science Foundation of China 61701548

Hubei Provincial Natural Science Foundation of China 2018CFB503

Fundamental Research Funds for the Central Universities, South-Central University for Nationalities CZQ17001

Fundamental Research Funds for the Central Universities, South-Central University for Nationalities CZZ18001

Fundamental Research Funds for the Central Universities, South-Central University for Nationalities CZY18046

More Information

Author Bio:
HOU Jian-Hua Professor at the College of Electronic Information Engineering, South-Central University for Nationalities. He received his Ph. D. degree in pattern recognition and intelligent system from Huazhong University of Science and Technology in 2007. His research interest covers computer vision and pattern recognition

ZHANG Guo-Shuai Master student at the College of Electronic Information Engineering, South-Central University for Nationalities. He received his bachelor degree from Changchun University in 2016. His research interest covers image processing and pattern recognition

Corresponding author: XIANG Jun Lecturer at the College of Electronic Information Engineering, South-Central University for Nationalities. She received her Ph. D. degree in control science and engineering from Huazhong University of Science and Technology in 2016. Her research interest covers computer vision and pattern recognition. Corresponding author of this paper

摘要

摘要: 近年来, 深度学习在计算机视觉领域的应用取得了突破性进展, 但基于深度学习的视频多目标跟踪(Multiple object tracking, MOT)研究却相对甚少, 而鲁棒的关联模型设计是基于检测的多目标跟踪方法的核心.本文提出一种基于深度神经网络和度量学习的关联模型:采用行人再识别(Person re-identification, Re-ID)领域中广泛使用的度量学习技术和卷积神经网络(Convolutional neural networks, CNNs)设计目标外观模型, 即利用三元组损失函数设计一个三通道卷积神经网络, 提取更具判别性的外观特征构建目标外观相似度; 再结合运动模型计算轨迹片间的关联概率.在关联策略上, 采用匈牙利算法, 首先以逐帧关联方式得到短小可靠的轨迹片集合, 再通过自适应时间滑动窗机制多级关联, 输出各目标最终轨迹.在2DMOT2015、MOT16公开数据集上的实验结果证明了所提方法的有效性, 与当前一些主流算法相比较, 本文方法取得了相当或者领先的跟踪效果.
- 多目标跟踪 /
- 深度学习 /
- 度量学习 /
- 关联模型 /
- 多级关联
Abstract: While deep learning has made a breakthrough in many sub-fields of computer vision recently, there are only a few deep learning approaches to multiple object tracking (MOT). Since the key component in detection based multiple object tracking is to design a robust affinity model, this paper proposes a novel affinity model based on deep neural network and metric learning, that is, metric learning, a widely used technique in the task of person re-identification (Re-ID), is exploited with convolutional neural networks (CNNs) to design the object's appearance model. Specifically, we adopt a three-channel CNNs that is learned by triplet loss function, to extract the discriminative appearance features and compute appearance similarity between objects. The appearance affinity is then combined with motion model to estimate associating probability among trajectories. A hierarchical association strategy is employed by the Hungarian algorithm. At the low level, a set of short but reliable tracklets are generated in a frame by frame fashion. These tracklets are then further associated to form longer tracklets at the higher levels via an adaptive sliding-window mechanism. Experiment results in the challenging MOT benchmark demonstrate the validity of the proposed method. Compared with several state-of-the-art approaches, our method has achieved competitive or superior performance.
- Multiple object tracking (MOT) /
- deep learning /
- metric learning /
- affinity model /
- multi-level association
Recommended by Associate Editor SANG Nong
注释:

1) 本文责任编委桑农

HTML全文

图 1 多目标跟踪方法整体框架

Fig. 1 The overall framework of multi-object tracking method

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图 2 三通道外观模型训练框图

Fig. 2 Three-channel appearance model training block diagram

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图 3 自适应时间滑动窗原理示意图

Fig. 3 Diagram of adaptive time sliding window principle

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图 4 多级关联中的运动模型示意图

Fig. 4 Diagram of motion model in multi-level association

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图 5 MOT16-01跟踪结果(从左到右依次为第121、174、248帧)

Fig. 5 Tracking results of MOT16-01 (121st, 174th, 248th frames from left to right)

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图 6 MOT16-03跟踪结果(从左到右依次为第249、307、424帧)

Fig. 6 Tracking results of MOT16-03 (249th, 307th, 424th frames from left to right)

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图 7 MOT16-07跟踪结果(从左到右依次为第397、455、500帧)

Fig. 7 Tracking results of MOT16-07 (397th, 455th, 500th frames from left to right)

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图 8 MOT16-06跟踪结果(从左到右依次为第537、806、1188帧)

Fig. 8 Tracking results of MOT16-06 (537th, 806th, 1188th frames from left to right)

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图 9 MVI_20032跟踪结果(从左到右依次为第332、360、423帧)

Fig. 9 Tracking results of MVI_20032 (332nd, 360th, 423rd frames from left to right)

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图 10 MVI_39771跟踪结果(从左到右依次为第1、54、113帧)

Fig. 10 Tracking results of MVI_39771 (1st, 54th, 113th frames from left to right)

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表 1 剥离对比实验结果

Table 1 Results of ablation study

Trackers	MOTA ($\uparrow$)	MOTP ($\uparrow$)	MT ($\uparrow$) (%)	ML ($\downarrow$) (%)	FP ($\downarrow$)	FN ($\downarrow$)	IDS ($\downarrow$)
A + T	19.5	74.6	7.41	66.70	109	14 202	43
M + T	17.6	74.6	7.40	64.80	307	14 326	70
A + M + T	21.0	74.3	9.26	70.40	175	13 893	16
A + M + V	14.7	75.1	1.85	67.00	60	14 804	339

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表 2 MOT16测试集结果

Table 2 Results of MOT16 test set

Trackers	Mode	MOTA ($\uparrow$)	MOTP ($\uparrow$)	MT ($\uparrow$) (%)	ML ($\downarrow$) (%)	FP ($\downarrow$)	FN ($\downarrow$)	IDS ($\downarrow$)	HZ ($\uparrow$)
AMIR^[20]	Online	47.2	75.8	14.0	41.6	2 681	92 856	774	1.0
CDA^[45]	Online	43.9	74.7	10.7	44.4	6 450	95 175	676	0.5
本文	Online	43.1	74.2	12.4	47.7	4 228	99 057	495	0.7
EAMTT^[41]	Online	38.8	75.1	7.9	49.1	8 114	102 452	965	11.8
OVBT^[42]	Online	38.4	75.4	7.5	47.3	11 517	99 463	1 321	0.3
[2mm] Quad-CNN^[17]	Batch	44.1	76.4	14.6	44.9	6 388	94 775	745	1.8
LIN1^[43]	Batch	41.0	74.8	11.6	51.3	7 896	99 224	430	4.2
CEM^[44]	Batch	33.2	75.8	7.8	54.4	6 837	114 322	642	0.3

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表 3 2DMOT2015测试集结果

Table 3 Results of 2DMOT2015 test set

Trackers	Mode	MOTA ($\uparrow$)	MOTP ($\uparrow$)	MT ($\uparrow$) (%)	ML ($\downarrow$) (%)	FP ($\downarrow$)	FN ($\downarrow$)	IDS ($\downarrow$)	HZ ($\uparrow$)
AMIR^[20]	Online	37.6	71.7	15.8	26.8	7 933	29 397	1 026	1.9
本文	Online	34.2	71.9	8.9	40.6	7965	31665	794	0.7
CDA^[45]	Online	32.8	70.7	9.7	42.2	4 983	35 690	614	2.3
RNN_LSTM^[24]	Online	19.0	71.0	5.5	45.6	11 578	36 706	1 490	165.2
Quad-CNN^[17]	Batch	33.8	73.4	12.9	36.9	7 879	32 061	703	3.7
MHT_DAM^[46]	Batch	32.4	71.8	16.0	43.8	9 064	32 060	435	0.7
CNNTCM^[22]	Batch	29.6	71.8	11.2	44.0	7 786	34 733	712	1.7
Siamese CNN^[19]	Batch	29.0	71.2	8.5	48.4	5 160	37 798	639	52.8
LIN1^[43]	Batch	24.5	71.3	5.5	64.6	5 864	40 207	298	7.5

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表 4 UA-DETRAC数据集跟踪结果

Table 4 Tracking results of UA-DETRAC dataset

	MOTA ($\uparrow$)	MOTP ($\uparrow$)	MT ($\uparrow$) (%)	ML ($\downarrow$) (%)	FP ($\downarrow$)	FN ($\downarrow$)	IDS ($\downarrow$)
车辆跟踪	65.3	78.5	75.0	8.3	1 069	481	27

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