注意力机制的多尺度单目标跟踪算法

doi:10.19665/j.issn1001-2400.2021.05.014

Abstract

Abstract:

In the process of single object tracking,due to problems of occlusion,disappearance and similar target interference,the tracking accuracy of the algorithm will be reduced.In order to solve these problems,a multi-scale single target tracking algorism based on the attention mechanism is proposed which uses asymmetric convolution to extract the multi-scale feature while reducing the parameters.It combines local features and global features to improve tracking robustness.The online update algorithm based on the attention mechanism is used which combines the response diagram and attention diagram to calculate a score which is used to weed out frames without targets.The attention mechanism strengthens the ability to distinguish the target and background,makes the network quickly adapt to the changes of the appearance of the targets,and improves the tracking performance of the algorithm.The algorithm is tested on OTB-100 datasets with other advanced tracking methods.Compared to the ATOM,the accuracy and success rate of our method are improved by 0.9% and 0.8% respectively and it is easier to retrieve the target after it is lost.

Key words: deep learning, single object tracking, attention, convolutional neural network

CLC Number:

TP37

SONG Jianfeng,MIAO Qiguang,WANG Chongxiao,XU Hao,YANG Jin. Multi-scale single object tracking based on the attention mechanism[J].Journal of Xidian University, 2021, 48(5): 110-116.

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References 17

[1]	孟琭, 杨旭. 目标跟踪算法综述[J]. 自动化学报, 2019, 45(7):1244-1260.
	MENG Lu, YANG Xu. A Survey of Object Tracking Algorithms[J]. Acta Automatica Sinica, 2019, 45(7):1244-1260.
[2]	孔晓东. 智能视频监控技术研究[D]. 上海:上海交通大学, 2008.
[3]	MA C, HUANG J B, YANG X K, et al. Hierarchical Convolutional Features for Visual Tracking[C]// IEEE International Conference on Computer Vision.Piscataway:IEEE, 2015:3074-3082.
[4]	BERTINETTO L, VALMADRE J, HENRIQUES J K, et al. Fully-Convolutional Siamese Networks for Object Tracking[C]// European Conference on Computer Vision.Heidelberg:Springer, 2016:850-865.
[5]	NAM H, HAN B. Learning Multi-Domain Convolutional Neural Networks for Visual Tracking[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Piscataway:IEEE, 2016:4293-4302.
[6]	DANELLJAN M, BHAT G, KHAN F S, et al. ATOM:Accurate Tracking by Overlap Maximization[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Piscataway:IEEE, 2019:4660-4669.
[7]	成磊, 王玥, 田春娜. 一种添加残差注意力机制的视觉目标跟踪算法[J]. 西安电子科技大学学报, 2020, 47(6):151-160.
	CHENG Lei, WANG Yue, TIAN Chunna. Residual Attention Mechanism for Visual Tracking[J]. Journal of Xidian University, 2020, 47(6):151-160.
[8]	SZEGEDY C, LIU W, JIA Y, et al. Going Deeper with Convolutions[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Piscataway:IEEE, 2015:1-9.
[9]	BOLME D S, BEVERIDGE J R, DRAPER B A, et al. Visual Object Tracking Using Adaptive Correlation Filters[C]// 23rd IEEE Conference on Computer Vision and Pattern Recognition.Piscataway:IEEE, 2010:2544-2550.
[10]	SZEGEDY C, VANHOUCKE V, IOFFE S, et al. Rethinking the Inception Architecture for Computer Vision[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition.Piscataway:IEEE, 2016:2818-2826.
[11]	GUO D, SHAO Y, CUI Y, et al. Graph attention tracking[C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Piscataway:IEEE, 2021:9543-9552.
[12]	PU S, SONG Y, MA C, et al. Deep Attentive Tracking Via Reciprocative Learning (2018)[J/OL]. [2018-10-15]https://arxiv.org/pdf/1810.03851/pdf.
[13]	HENRIQUES J F, CASEIRO R, MARTINS P, et al. High-Speed Tracking with Kernelized Correlation Filters[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2015, 37(3):583-596.
[14]	HE K, ZHANG X Y, REN S Q, et al. Deep Residual Learning for Image Recognition[C]// IEEE Conference on Computer Vision and Pattern Recognition.Piscataway:IEEE, 2016:770-778.
[15]	WU Y, LIM J, YANG M H. Online Object Tracking:A Benchmark[C]// 2013 IEEE Conference on Computer Vision and Pattern Recognition.Piscataway:IEEE, 2013:2411-2418.
[16]	SONG Y, MA C, GONG L, et al. Crest:Convolutional Residual Learning for Visual Tracking[C]// Proceedings of the IEEE International Conference on Computer Vision.Piscataway:IEEE, 2017:2555-2564.
[17]	BERTINETTO L, VALMADRE J, HENRIQUES J F, et al. Fully-Convolutional Siamese Networks for Object Tracking[C]// European Conference on Computer Vision.Heidelberg:Springer, 2016:850-865.

Multi-scale single object tracking based on the attention mechanism

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