基于步态的摄像机网络跨视域行人跟踪

宋淑婕; 万九卿

doi:10.13700/j.bh.1001-5965.2021.0610

基于步态的摄像机网络跨视域行人跟踪

doi: 10.13700/j.bh.1001-5965.2021.0610

宋淑婕,
万九卿^,

北京航空航天大学自动化科学与电气工程学院，北京 100191

基金项目: 北京市自然科学基金(4192031)；国家自然科学基金(61873015)

详细信息

作者简介:
宋淑婕女，硕士研究生。主要研究方向：目标检测跟踪与识别

万九卿男，博士，副教授，硕士生导师。主要研究方向：信号处理、目标检测跟踪与识别

通讯作者:
E-mail：wanjiuqing@buaa.edu.cn

中图分类号: TP391
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- 被引次数: 0
出版历程
- 收稿日期: 2021-10-18
- 录用日期: 2021-12-10
- 网络出版日期: 2022-01-26
- 整期出版日期: 2023-08-31

Gait based cross-view pedestrian tracking with camera network

SONG Shujie,
WAN Jiuqing^,

School of Automation Science and Electrical Engineering，Beihang University，Beijing 100191，China

Funds: Beijing Municipal Natural Science Foundation (4192031); National Natural Science Foundation of China (61873015)

More Information

Corresponding author: E-mail：wanjiuqing@buaa.edu.cn

摘要

摘要:
非重叠视域摄像机网络行人目标跨视域跟踪是智能视觉监控的基本问题之一。针对基于外观一致性假设的行人跨视域跟踪方法对光照或衣着变化敏感的问题，提出一种融合基于2D骨架图的步态特征与时空约束的跨视域行人跟踪方法。从单视域局部轨迹提取骨架集合计算步态特征，建立跨视域目标跟踪问题的整数线性规划模型，模型参数由步态特征相似度和时空约束定义，利用对偶分解算法实现问题的分布式求解。通过步态特征与更加精细化的时空约束融合，显著提升了仅基于步态特征的跨视域跟踪算法对于光照和衣着变化的鲁棒性，克服了单独使用步态或时空特征时判别力较弱的问题。在公开数据集上的测试结果表明，所提方法跟踪准确，且对光照和衣着变化具有鲁棒性。
- 步态特征 /
- 时空约束 /
- 对偶分解 /
- 跨视域行人跟踪 /
- 智能视觉监控
Abstract:
Pedestrian tracking across non-overlapping camera views is one of the basic problems of intelligent visual surveillance. A cross-view pedestrian target tracking method based on the gait features of a 2D skeleton diagram and space-time constraints is proposed in order to address the issue that the pedestrian cross-view tracking method based on the assumption of appearance consistency is sensitive to lighting or clothing changes. The skeleton set is extracted from the local trajectory of the single view to calculate the gait features, and the integer programming model of the cross-view target tracking problem is established. The model parameters are defined by the similarity of the gait features and the space-time constraints. The dual decomposition algorithm is used to realize the distributed solution to the above problems. The algorithm’s robustness to changes in lighting and clothing is greatly increased through the combination of gait features and more precise space-time restrictions, and it also solves the issue of weak discriminating when gait or space-time features are employed alone. The test results on the public data sets show that the proposed method is accurate in tracking and robust to lighting and clothing changes.
- gait feature /
- space-time constraints /
- dual decomposition /
- cross-view pedestrians tracking /
- intelligent visual surveillance

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图 1 智能摄像机网络及其拓扑

Figure 1. A smart camera network and its topology

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图 2 目标通过时摄像机收集到的视频帧

Figure 2. Video frames collected by camera as target passes

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图 3 说明示例

Figure 3. Illustrating example

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图 4 修正的骨架模型

Figure 4. Modified skeleton model

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图 5 生成骨架图像

Figure 5. Skeleton image generated

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图 6 一个步态周期的骨架图像集合

Figure 6. Set of skeleton images in one walking cycle

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图 7 对偶分解算法中的二分图子问题

Figure 7. Bipartite graph subproblem in dual decomposition algorithm

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图 8 摄像机网络的布局和视域

Figure 8. Layouts of camera networks and camera’s FOV

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图 9 光照变化效果

Figure 9. Effects of lighting variation

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图 10 换装效果

Figure 10. Effect of clothed changing

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图 11 VBOLO数据集拍摄场景

Figure 11. Two stations of VBOLO dataset

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图 12 演员在2个场景出现的示例^[33]

Figure 12. Examples of actors appearing in two stations

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图 13 9个演员Rank-1~Rank-10查询结果的对应观测

Figure 13. Corresponding observations of 9 actors from Rank-1 to Rank-10

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图 14 光照改变下跨视域跟踪示例

Figure 14. Examples of cross-view tracking under lighting variations

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图 15 换装情况下跨视域跟踪示例

Figure 15. Examples of cross-view tracking under clothing changes

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表 1 时空观测

Table 1. Space-time observation

时空属性	状态
位置	CamE
进入的时间	09:10:21 a.m.
离开的时间	09:10:27 a.m.
进入的方向	左边界
离开的方向	右边界

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表 2 NLPR_MCT数据集的细节

Table 2. Details of NLPR_MCT dataset

子数据集	相机数	持续时间/min	帧率/（帧·s⁻¹）	目标数	$T{P_s}$	$ T{P_c} $
Dataset1	3	20	20	235	71853	334
Dataset2	3	20	20	255	88419	408
Dataset3	4	3.5	25	14	18187	152
Dataset4	4	24	25	49	42615	256
注：TP_s为数据集提供的单视域的轨迹数，TP_c为数据集提供的跨视域的轨迹数。

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表 3 MCT及模拟数据集上Rank-n准确度比较

Table 3. Comparison of Rank-n accuracy on MCT and simulated datasets %

子数据集	Rank-1									Rank-5
	原始			光照			换装			原始			光照			换装
	RGB	SSP	步态	RGB	SSP	步态	RGB	SSP	步态	RGB	SSP	步态	RGB	SSP	步态	RGB	SSP	步态
Dataset1	6.35	45.5	17.86	2.59	2.6	26.42	0.53	3.2	17.53	15.34	54.5	40.82	4.15	4.2	41.97	7.41	7.9	38.14
Dataset2	8.89	50.4	11.96	0.73	1.1	8.99	0.37	1.1	11.15	16.67	64.1	26.45	2.19	3.0	17.27	2.96	5.2	23.38
Dataset3	19.08	69.1	35.1	16.11	60.5	35.1	9.21	13.8	29.8	53.29	85.5	65.56	47.65	78.3	62.25	35.53	55.3	65.56
Dataset4	21.14	85.5	40.73	14.11	55.0	36.95	5.62	8.4	36.95	41.87	95.6	66.53	37.9	74.3	64.66	24.1	34.9	61.04

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表 4 VBOLO数据集上步态特征Rank-n准确度比较

Table 4. Comparison of Rank-n accuracyof gait feature on VBOLO dataset %

Rank-1	Rank-5	Rank-10	Rank-15	Rank-20
56.57	91.92	97.98	98.99	100

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表 5 跨视域跟踪方法的性能比较

Table 5. Performance comparison of cross-view tracking methods

方法	${ {{e} } }^{ {\rm{c} } }$				W
方法	Dataset1	Dataset2	Dataset3	Dataset4	Dataset1	Dataset2	Dataset3	Dataset4
ICLM^[37]	13	30	32	62	0.961	0.927	0.790	0.758
CRF^[37]	54	81	51	70	0.838	0.801	0.665	0.727
EGM^[29]	55	121	39	157	0.8353	0.7034	0.7417	0.3845
PMCSHR^[38]	112	167	44	110	0.662	0.591	0.711	0.633
Hfutdspmct^[30]	86	141	40	155	0.7425	0.6544	0.7368	0.3945
AdbTeam^[30]	227	267	131	216	0.3204	0.3456	0.1382	0.1563
TRACTA^[39]	121	176	126	181	0.6327	0.5448	0.1398	0.2870
本文	24	81	85	95	0.9279	0.8014	0.4370	0.6286

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表 6 光照改变和换装情况下基于步态、RGB和SSP特征的跨视域跟踪方法结果对比

Table 6. Comparison of results of cross-view tracking methods based on gait, RGB and SSP feature under lighting variations and clothes changing

子数据集	$e^{ {\rm{c} } }$									W
	原始			光照改变			换装			原始			光照改变			换装
	RGB	SSP	步态	RGB	SSP	步态	RGB	SSP	步态	RGB	SSP	步态	RGB	SSP	步态	RGB	SSP	步态
Dataset1	75	18	30	83	43	26	54	59	30	0.775 4	0.945 8	0.908 9	0.751 4	0.870 8	0.921 8	0.838 2	0.823 3	0.909 5
Dataset2	140	39	56	132	0.904 4	57	114	73	62	0.656 8	114	0.860 9	0.674 0	0.818 8	0.859 5	0.720 5	0.719 9	0.847 2
Dataset3	120	36	55	86	65	55	94	95	60	0.210 5	0.699 9	0.517 5	0.358 2	0.488 2	0.533 9	0.381 5	0.374 9	0.508 2
Dataset4	164	36	78	151	81	100	115	114	82	0.359 2	0.859 0	0.695 0	0.403 0	0.678 3	0.601 4	0.550 6	0.554 5	0.675 6

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表 7 有无时空约束的结果比较

Table 7. Comparison of results with and without space-time constraints

有/无时控约束	$e^{ {\rm{c} } }$				W
有/无时控约束	Dataset1	Dataset2	Dataset3	Dataset4	Dataset1	Dataset2	Dataset3	Dataset4
无时空约束	276	342	120	203	0.159 9	0.148 2	0.195 7	0.200 7
有时空约束	30	56	55	78	0.908 9	0.860 9	0.517 5	0.695 0

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表 8 基于剪影和基于骨架的步态特征对比

Table 8. Silhouette vs skeleton based gait feature

输入	$e^{ {\rm{c} } }$				W
输入	Dataset1	Dataset2	Dataset3	Dataset4	Dataset1	Dataset2	Dataset3	Dataset4
剪影	37	88	93	110	0.891 5	0.782 4	0.365 7	0.551 2
骨架	30	56	55	78	0.908 9	0.860 9	0.517 5	0.695 0

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