表观增强的深度目标跟踪算法

doi:10.13229/j.cnki.jdxbgxb20210367

Abstract

Abstract:

Aiming at the problem that the research of video real-time target tracking based on deep neural network mainly focuses on the optimization of backbone network, and the design and training are relatively complex, a new apparent enhanced depth target tracking algorithm with apparent enhancement was proposed. Firstly, by introducing simple and easy-to-implement traditional apparent features, it is directly fused with deep semantic features, which enhances the discrimination ability of objects within a class.Secondly,through voting mechanisms and adaptive search modules, the robustness of tracking algorithm is enhanced. The test results on the VOT series data set show that compared with the benchmark algorithm, the average overlap expectation (EAO) of the proposed algorithm has been improved by 2%~4%, and the accuracy and robustness have reached or even partially exceeded the existing complex optimization algorithms.

Key words: computer vision, siamese network, apparent information, voting mechanism, adaptive search

CLC Number:

TP391

Kan WANG,Hang SU,Hao ZENG,Jian QIN. Deep target tracking using augmented apparent information[J].Journal of Jilin University(Engineering and Technology Edition), 2022, 52(11): 2676-2684.

Figures/Tables 14

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Table 1

Fig.6

Table 2

Table 3

Fig.7

Table 4

Table 5

Fig.8

Table 6

References 19

1	徐谦,李颖,王刚.基于深度学习的行人和车辆检测[J].吉林大学学报:工学版, 2019, 49(5): 1661-1667.
	Xu Qian, Li Ying, Wang Gang, Pedestrian-vehicle detection based on deep learning[J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(5): 1661-1667.
2	Li B, Yan J, Wu W, et al. High performance visual tracking with siamese region proposal network[C]∥IEEE Conference on Computer Vision and Pattern Recognition, Utah, USA, 2018: 8971-8980.
3	Matej K, Ales L, Jiri M, et al. The sixth visual object tracking VOT2018 challenge results[C]∥European Conference on Computer Vision, Munich, Germany, 2018: 3-53.
4	Wang Q, Li B. SiamRPN++: evolution of siamese visual tracking with very deep networks[C]∥IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, USA, 2019: 4277-4286.
5	Zhang Z P, Peng H W. Deeper and wider siamese networks for real-time visual tracking[C]∥IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, USA, 2019: 4586-4595.
6	徐涛, 马克, 刘才华. 基于深度学习的行人多目标跟踪方法[J]. 吉林大学学报: 工学版, 2021, 51(1): 27-38.
	Xu Tao, Ma Ke, Liu Cai-hua. Multi object pedestrian tracking based on deep learning[J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(1): 27-38.
7	Voigtlaender P, Luiten J, Torr P H S, et al. Siam R-CNN: visual tracking by re-detection[C]∥IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, USA, 2020: 6577-6587.
8	Xu Y D, Wang Z Y, Li Z, et al. SiamFC++: towards robust and accurate visual tracking with target estimation guidelines[C]// Proceedings of the AAAI Conference on Artificial Intelligence, New York, USA, 2020: 12549-12556.
9	He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]∥IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 770-778.
10	Kristan M, Leonardis A, Matas J, et al. The visual object tracking VOT2016 challenge results[C]∥IEEE International Conference on Computer Vision Workshops, Santiago, Chile, 2015: 607-628.
11	Kristan M, Leonardis A, Matas J, et al.The seventh visual object tracking VOT2019 challenge results[C]∥IEEE International Conference on Computer Vision Workshop, Seoul, Korea, 2019: 2206-2241.
12	Martin D, Goutam B, Fahad S K, et al. ATOM: accurate tracking by overlap maximization[C]∥Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, USA, 2019: 4660-4669.
13	Wang Q, Zhang L, Luca B, et al. Fast online object tracking and segmentation: a unifying approach[C]∥Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, USA, 2019: 1328-1338.
14	Anelljan M, Robinson A, Khan F S, et al. Beyond correlation filters: learning continuous convolution operators for visual tracking[C]∥European Conference on Computer Vision, Amsterdam, Netherlands, 2016: 472-488.
15	Xu T, Feng Z H, Wu X J, et al. Learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking[J]. IEEE Transactions on Image Processing, 2019, 28(11): 5596-5609.
16	Goutam B, Martin D, Luc V G, et al. Learning discriminative model prediction for tracking[C]∥Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Korea, 2019: 6182-6191.
17	薛英杰,韩威,周松斌,等.基于生成对抗单分类网络的异常声音检测[J].吉林大学学报:理学版,2021,59(6):1517-1524.
	Xue Ying-jie, Han Wei, Zhou Song-bin,et al. Abnormal sound detection based on generative adversarial single classification network[J]. Journal of Jilin University(Science Edition),2021,59(6):1517-1524.
18	朱新丽,才华,寇婷婷,等.行人多目标跟踪算法[J]. 吉林大学学报:理学版,2021,59(5):1161-1170.
	Zhu Xin-li, Cai Hua, Kou Ting-ting, et al. Pedestrian multi-target tracking algorithm[J]. Journal of Jilin University(Science Edition),2021,59(5):1161-1170.
19	龙年.基于轨迹提取算法的视频关联动作跟踪仿真[J].吉林大学学报:理学版,2022,60(3):641-646.
	Long Nian. Simulation of video association motion tracking based on trajectory extraction algorithm[J]. Journal of Jilin University(Science Edition),2022,60(3):641-646.

Related Articles 15

[1]	Yong LIU,Lei XU,Chu-han ZHANG. Deep reinforcement learning model for text games [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(3): 666-674.
[2]	Jie CAO,Xue QU,Xiao-xu LI. Few⁃shot image classification method based on sliding feature vectors [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(5): 1785-1791.
[3]	Tao XU,Ke MA,Cai-hua LIU. Multi object pedestrian tracking based on deep learning [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(1): 27-38.
[4]	Hong-wei ZHAO,Ming-zhao LI,Jing LIU,Huang-shui HU,Dan WANG,Xue-bai ZANG. Scene classification based on degree of naturalness and visual feature channels [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(5): 1668-1675.
[5]	CHE Xiang-jiu, WANG Li, GUO Xiao-xin. Improved boundary detection based on multi-scale cues fusion [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1621-1628.
[6]	WANG Yi-yuan, OUYANG Dan-tong, ZHANG Li-ming. Min-length hitting set GRASP algorithm based on dynamic degree of components [J]. 吉林大学学报(工学版), 2017, 47(3): 930-936.
[7]	XU Yan-yan, CHEN Hui, LIU Jia-ju, YUAN Jin-zhao. Cell processor stereo matching parallel computation [J]. 吉林大学学报(工学版), 2017, 47(3): 952-958.
[8]	ZHANG Bao-hua, HUANG Wen-qian, LI Jiang-bo, ZHAO Chun-jiang, LIU Cheng-liang, HUANG Dan-feng. Online sorting of irregular potatoes based on I-RELIEF and SVM method [J]. 吉林大学学报(工学版), 2014, 44(6): 1811-1817.
[9]	YANG Yan, LIU Sa, LIAN Shi-bin, ZHU Xiao-dong. Analysis of fruit tree pests morphological characteristics based on computer vision [J]. 吉林大学学报(工学版), 2013, 43(增刊1): 235-238.
[10]	SHANG Fei, MA Jun-Xiao, YAO Li, TIAN Di, QIU Chun-Ling. Multifeature fusion based method for monitoring working status of instruments [J]. 吉林大学学报(工学版), 2010, 40(02): 545-0548.
[11]	GE Liang, ZHU Qing-sheng, FU Si-si, LUO Da-jiang, LIU Jin-feng. Improved image dense stereo matching algorithm [J]. 吉林大学学报(工学版), 2010, 40(01): 212-0217.
[12]	GUAN Xin, JIA Xin, GAO Zhen-hai . Adaptive threshold algorithm based on contrast-regional homogeneity analysis of lane image [J]. 吉林大学学报(工学版), 2008, 38(04): 758-763.
[13]	Wan Peng，Sun Yu，Sun Yong-hai . Recognition method of rice kernel shape based on computer vision [J]. 吉林大学学报(工学版), 2008, 38(02): 489-0492.
[14]	Tian Jian, Li Jiang, Li Yaqiao. Calibration technique of photogrammetry of traffic accident scene [J]. 吉林大学学报(工学版), 2006, 36(增刊1): 136-0139.
[15]	GUAN Xin, DONG Yin-ping, GAO Zhen-hai. LMedSquare based road curve fitting algorithm [J]. 吉林大学学报(工学版), 2004, (2): 194-197.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

特征	Lost	Accuracy	Robustness	EAO
Baseline（原始）	50	0.601	0.234	0.415
颜色特征	48	0.604	0.225	0.417
HOG特征	49	0.602	0.229	0.422
LBP特征	49	0.602	0.229	0.415

VOT2018	基线	搜索区自适应缩小	搜索区自适应扩大	搜索区自适应放缩
Lost	50	51	51	50
Accuracy	0.601	0.605	0.605	0.600
Robustness	0.234	0.239	0.239	0.234
EAO	0.415	0.400	0.404	0.398

VOT2018	基线	搜索区自适应缩小	搜索区自适应扩大a	搜索区自适应扩大b	搜索区自适应放缩a	搜索区自适应放缩b
Lost	50	45	48	49	43	44
Accuracy	0.601	0.594	0.609	0.603	0.601	0.595
Robustness	0.234	0.211	0.225	0.229	0.201	0.206
EAO	0.415	0.438	0.417	0.422	0.441	0.446

颜色	HOG	LBP	自适应搜索区域策略	Lost	Accuracy	Robustness	EAO
√				48	0.604	0.225	0.417
	√			49	0.602	0.229	0.422
		√		49	0.602	0.229	0.415
√			√	43	0.595	0.201	0.450
	√		√	43	0.597	0.201	0.453
		√	√	48	0.595	0.225	0.416

数据集		颜色+自适应搜索	HOG+自适应搜索	LBP+自适应搜索	SiamRPN++
VOT2016	Accuracy	0.636	0.638	0.624	0.642
	Robustness	0.168	0.172	0.172	0.196
	EAO	0.467	0.483	0.469	0.464
VOT2018	Accuracy	0.595	0.597	0.595	0.600
	Robustness	0.201	0.201	0.225	0.234
	EAO	0.450	0.453	0.416	0.414

Deep target tracking using augmented apparent information

RICH HTML

PDF (PC)