结合粒子滤波及度量学习的目标跟踪方法

doi:10.11959/j.issn.1000-436x.2021087

Abstract

Abstract:

Focusing on the issue of the significant degradation of target tracking performance caused by adverse factors in complex environment, a target tracking method based on particle filtering and metric learning was proposed.First of all, a convolutional neural network (CNN) was offline-trained via the proposed method to effectively obtain the target characteristics.After that, the distance measurement matrix optimization model to minimize the prediction error could be constructed on the basis of the metric learning for kernel regression (MLKR) method, and the resultant model could be handled via using the gradient descent approach to obtain the optimal solution of the candidate target.Moreover, based on the predicted value of the optimal candidate target, the reconstruction error was calculated to construct the target observation model.Finally, a long-short-term update strategy was introduced to achieve the effective target tracking under the particle filter tracking framework.The experiment results show that the proposed method has higher tracking accuracy and better robustness in complex environments.

Key words: target tracking, particle filter, convolutional neural network, metric learning, sparse representation

CLC Number:

TP391

Hongyan WANG, Libin ZHANG, Guoqiang CHEN, Zumin WANG, Zhiyuan GUAN. Approach of target tracking combining particle filter and metric learning[J]. Journal on Communications, 2021, 42(5): 98-110.

Figures/Tables 12

测试序列	所提方法	CNN-PF	Struck	CT	TLD	DFT	BACF
MotorRolling	$0 . 71$	0.48	0.19	0.21	0.27	0.16	$\underline{0.67}$
Jogging	$0 . 81$	$\underline{0.75}$	0.19	0.11	0.66	0.21	0.61
Boy	$0 . 82$	$\underline{0.76}$	0.65	0.39	0.61	0.67	0.56
Skating1	$0 . 48$	0.35	$\underline{0.43}$	0.04	0.31	0.06	0.24
Matrix	$0 . 65$	$\underline{0.45}$	0.01	0.02	0.16	0.02	0.37
Bird2	$0 . 77$	0.57	0.37	0.54	$\underline{0.61}$	0.59	0.52
Tiger2	$0 . 77$	0.65	0.18	0.13	0.24	0.63	$\underline{0.74}$
Basketball	$0 . 72$	0.51	0.35	$\underline{0.57}$	0.55	0.54	0.55
Singer1	$0 . 68$	0.33	0.39	0.45	$\underline{0.51}$	0.38	0.47
Singer2	$0 . 63$	$\underline{0.57}$	0.35	0.29	0.48	0.39	0.54
平均值	$0 . 70$	$\underline{0.54}$	0.31	0.28	0.44	0.37	0.52

测试序列	所提方法	CNN-PF	Struck	CT	TLD	DFT	BACF
MotorRolling	$15 . 95$	$\underline{26.56}$	148.55	132.75	79.81	164.54	148.55
Jogging	$3 . 27$	15.14	107.93	139.63	13.53	33.76	$\underline{5.33}$
Boy	$1 . 57$	1.96	3.36	34.03	3.60	2.42	1.58
Skating1	$\underline{13.68}$	$9 . 33$	65.58	180.11	125.17	178.15	140.10
Matrix	$10 . 87$	$\underline{24.41}$	221.08	85.94	57.23	206.55	113.30
Bird2	$10 . 11$	44.17	65.09	$\underline{17.91}$	37.61	47.78	21.59
Tiger2	$11 . 56$	$\underline{14.06}$	66.82	70.22	53.32	14.25	21.52
Basketball	$15 . 51$	$\underline{16.02}$	80.93	121.33	72.45	181.29	49.60
Singer1	$19 . 73$	55.21	71.92	40.20	$\underline{25.53}$	49.53	69.04
Singer2	$\underline{12.75}$	$9 . 82$	66.39	115.23	47.28	51.86	57.57
平均值	$11 . 50$	$\underline{21.67}$	89.77	93.74	51.56	92.49	62.81

测试序列	所提方法	CNN-PF	Struck	CT	TLD	DFT	BACF
MotorRolling	19.21	7.96	12.14	$46 . 53$	31.87	2.82	18.27
Jogging	4.13	8.92	12.16	$58 . 33$	17.03	8.27	19.86
Boy	16.39	8.38	12.81	$26 . 43$	16.05	5.38	22.46
Skating1	8.71	8.54	9.73	$37 . 69$	24.71	4.38	18.81
Matrix	9.23	10.12	8.24	$30 . 78$	23.17	6.35	21.01
Bird2	32.38	19.31	15.77	$42 . 12$	27.32	5.56	23.16
Tiger2	28.17	13.79	6.32	$43 . 86$	9.37	4.60	21.52
Basketball	19.65	10.44	5.59	$41 . 34$	21.11	8.65	28.37
Singer1	13.13	9.37	11.99	$38 . 83$	12.26	1.81	16.25
Singer2	9.91	10.62	6.58	$40 . 93$	27.63	3.28	16.66
平均	16.09	10.74	10.13	$40 . 68$	21.05	5.11	20.63

References 31

[1]	ZHANG N N , WU C X , WU Y ,et al. An improved target tracking algorithm and its application in intelligent video surveillance system[J]. Multimedia Tools and Applications, 2020,79(23/24): 15965-15983.
[2]	FRUHWIRTH-REISINGER C , KRISPEL G , POSSEGGER H ,et al. Towards data-driven multi-target tracking for autonomous driving[C]// Computer Vision Winter Workshop.[S.n.:s.l.], 2020.
[3]	YUAN M Z . An analysis model of sports human body based on computer vision tracking technology[J]. DEStech Transactions on Social Science,Education and Human Science, 2017.
[4]	BAISA N L . Robust online multi-target visual tracking using a HISP filter with discriminative deep appearance learning[J]. Journal of Visual Communication and Image Representation, 2021,77: 102952.
[5]	王铮, 赵晓, 佘宏杰 ,等. 基于双目视觉的AGV障碍物检测与避障[J]. 计算机集成制造系统, 2018,24(2): 400-409.
	WANG Z , ZHAO X , SHE H J ,et al. Obstacle detection and obstacle avoidance of AGV based on binocular vision[J]. Computer Integrated Manufacturing Systems, 2018,24(2): 400-409.
[6]	PAN Z , LIU S , FU W N . A review of visual moving target tracking[J]. Multimedia Tools and Applications, 2017,76(16): 16989-17018.
[7]	KRISTAN M , LEONARDIS A , MATAS J ,et al. The visual object tracking vot2017 challenge results[C]// 2017 the IEEE International Conference on Computer Vision Workshop. Piscataway:IEEE Press, 2017: 1949-1972.
[8]	WANG N , YEUNG D Y . Learning a deep compact image representation for visual tracking[C]// Advances in Neural Information Processing Systems. Massachusetts:MIT Press, 2013: 809-817.
[9]	WANG L J , OUYANG W L , WANG X G ,et al. Visual tracking with fully convolutional networks[C]// 2015 IEEE International Conference on Computer Vision. Piscataway:IEEE Press, 2015: 3119-3127.
[10]	ZHANG K H , LIU Q S , WU Y ,et al. Robust visual tracking via convolutional networks without training[C]// IEEE Transactions on Image Processing. Piscataway:IEEE Press, 2016: 1779-1792.
[11]	MOZHDEHI R J , MEDEIROS H . Deep convolutional particle filter for visual tracking[C]// 2017 IEEE International Conference on Image Processing. Piscataway:IEEE Press,, 2017: 3650-3654.
[12]	HU J L , LU J W , TAN Y P . Deep metric learning for visual tracking[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2016,26(11): 2056-2068.
[13]	WEINBERGER K Q , TESAURO G . Metric learning for kernel regression[C]// 2007 Artificial Intelligence and Statistics.[S.n.:s.l.], 2007: 612-619.
[14]	HUANG R Q , SUN S L . Kernel regression with sparse metric learning[J]. Journal of Intelligent ＆ Fuzzy Systems, 2013,24(4): 775-787.
[15]	NADARAYA E A . On estimating regression[J]. Theory of Probability＆ Its Applications, 1964,9(1): 141-142.
[16]	GUTIERREZ E D , LEVY R , BERGEN B . Finding non-arbitrary form-meaning systematicity using string-metric learning for kernel regression[C]// Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics.[S.n.:s.l.], 2016: 2379-2388.
[17]	WANG N , YEUNG D . Learning a deep compact image representation for visual tracking[C]// Neural Information Processing Systems.[S.n.:s.l.], 2013: 809-817.
[18]	POLSON N , SOKOLOV V . Bayesian particle tracking of traffic flows[J]. IEEE Transactions on Intelligent Transportation Systems, 2018,19(2): 345-356.
[19]	ROSS D A , LIM J , LIN R S ,et al. Incremental learning for robust visual tracking[J]. International Journal of Computer Vision, 2008,77(1): 125-141.
[20]	XIE S C , ZHANG X H , CAI J . Video crowd detection and abnormal behavior model detection based on machine learning method[J]. Neural Computing and Applications, 2019,31(1): 175-184.
[21]	KRIZHEVSKY A , SUTSKEVER I , HINTON G E . ImageNet classification with deep convolutional neural networks[C]// Advances in Neural Information Processing Systems.[S.n.:s.l.], 2012: 1097-1105.
[22]	HE Z Y , YI S Y , CHEUNG Y M ,et al. Robust object tracking via key patch sparse representation[J]. IEEE Transactions on Cybernetics, 2017,47(2): 354-364.
[23]	王洪雁, 邱贺磊, 郑佳 ,等. 光照变化下基于逆向稀疏表示的视觉跟踪方法[J]. 电子与信息学报, 2019,41(3): 632-639.
	WANG H Y , QIU H L , ZHENG J ,et al. Visual tracking method based on reverse sparse representation under illumination variation[J]. Journal of Electronics ＆ Information Technology, 2019,41(3): 632-639.
[24]	WU Y , LIM J , YANG M H . Object tracking benchmark[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015,37(9): 1834-1848.
[25]	KRISTAN M , LEONARDIS A , MATAS J ,et al. The sixth visual object tracking VOT2018 challenge results[J]// European Conference on Computer Vision.Berlin:Springer, 2018: 3-53.
[26]	李位星, 马维亮, 田卉 ,等. 基于CNN的粒子滤波目标跟踪算法研究[J]. 北京理工大学学报, 2018,38(12): 1256-1262.
	LI W X , MA W L , TIAN H ,et al. Particle filter for object tracking based on CNN feature[J]. Transactions of Beijing Institute of Technology, 2018,38(12): 1256-1262.
[27]	HARE S , GOLODETZ S , SAFFARI A ,et al. Struck:structured output tracking with kernels[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016,38(10): 2096-2109.
[28]	ZHANG K H , ZHANG L , YANG M H ,et al. Real-time compressive tracking[C]// European Conference on Computer Vision. Berlin:Springer, 2012: 864-877.
[29]	KALAL Z , MATAS J , MIKOLAJCZYK K . P-N learning:Bootstrapping binary classifiers by structural constraints[C]// 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2010: 49-56.
[30]	SEVILLA-LARA L , LEARNED-MILLER E . Distribution fields for tracking[C]// 2012 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2012: 1910-1917.
[31]	GALOOGAHI H K , FAGG A , LUCEY S . Learning background-aware correlation filters for visual tracking[C]// 2017 IEEE International Conference on Computer Vision. Piscataway:IEEE Press, 2017: 1144-1152.

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测试序列	光照变化	比例变化	运动模糊	旋转	遮挡	快速运动	背景杂波
MotorRolling	√	√	√	×	×	×	√
Jogging	×	×	×	×	√	×	×
Boy	×	√	×	√	×	√	×
Skating1	√	√	√	√	√	√	√
Matrix	×	√	×	√	×	√	√
Bird2	×	×	√	√	×	√	√
Tiger2	×	×	√	×	√	√	√
Basketball	×	×	√	×	√	√	√
Singer1	√	√	√	×	×	×	√
Singer2	√	×	√	×	×	√	√

独立仿真项	测试序列
独立仿真项	Boy	MotorRolling
CNN+粒子滤波+长短时更新	0.76	0.64
CNN+MLKR +长短时更新	0.77	0.67
CNN+粒子滤波+MLKR	0.80	0.69
CNN+MLKR+粒子滤波+长短时更新	0.82	0.71

Approach of target tracking combining particle filter and metric learning

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