Target Tracking Algorithm Based on Siamese Region Proposal Network for UAV

doi:10.19678/j.issn.1000-3428.0060045

Abstract

Abstract: When performing target tracking tasks, Unmanned Aerial Vehicles(UAV) often lose their targets due to occlusion, illumination changes and background interference.Based on the SiamRPN algorithm, a target tracking algorithm is proposed for UAV.The algorithm first adds a strip pooling and a global context module to the network, which establishs remote context and enables it to adapt to different tracking scenarios.Then the calculation method based on cross-comparison optimization is used to complete the feature extraction of the target, and to return an accurate prediction boundary frame.The experimental results on the UAV123 public data set benchmark show that compared to SiamRPN, SiamFC, SAMF algorithms, the algorithm exhibits better tracking performance and high robustness.Compared to SiamRPN algorithm, the accuracy rate and success rate of the algorithm are improved by 6.45% and 11.63% respectively in the background interference.

Key words: target tracking, Unmanned Aerial Vehicle(UAV), Deep Learning(DL), siamese network, attention mechanism

摘要： 在无人机跟踪过程中，遮挡、光照变化、背景干扰等影响会导致跟踪目标丢失。基于SiamRPN算法提出一种无人机目标跟踪算法。通过在网络中加入空间条带池和全局上下文模块建立远程上下文关系，以适应不同的跟踪场景。同时利用改进交并比的计算方法提取目标特征，并回归精准的预测框。在UAV123数据集上的实验结果表明，相比SiamRPN、SiamFC、SAMF等算法，该算法的跟踪性能较优且具有较强的鲁棒性，尤其在背景干扰环境下，其精确率和成功率较SiamRPN算法分别提升了6.54%和11.63%。

关键词: 目标跟踪, 无人机, 深度学习, 孪生网络, 注意力机制

CLC Number:

TP391

YANG Shuaidong, CHEN Haiyun, XU Fancheng, ZHAO Shuduo, YUAN Jiemin. Target Tracking Algorithm Based on Siamese Region Proposal Network for UAV[J]. Computer Engineering, 2022, 48(1): 288-295,304.

杨帅东, 谌海云, 徐钒诚, 赵书朵, 袁杰敏. 基于孪生区域建议网络的无人机目标跟踪算法[J]. 计算机工程, 2022, 48(1): 288-295,304.

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References

[1] TIAN B, YAO Q, GU Y, et al.Video processing techniques for traffic flow monitoring:a survey[C]//Proceedings of the 14th International IEEE Conference on Intelligent Transportation Systems.Washington D.C., USA:IEEE Press, 2011:1103-1108.
[2] YILMAZ A, JAVED O, SHAH M.Object tracking:a survey[J].ACM Computing Surveys, 2006, 38(4):1-45.
[3] VALMADRE J, BERTINETTO L, HENRIQUES J, et al.End-to-end representation learning for correlation filter based tracking[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washingston D.C., USA:IEEE Press, 2017:2805-2813.
[4] COMANICIU D, RAMESH V, MEER P.Real-time tracking of non-rigid objects using mean shift[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2000, 2:142-149.
[5] BOLME D S, BEVERIDGE J R, DRAPER B A, et al.Visual object tracking using adaptive correlation filters[C]//Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2010:2544-2550.
[6] HENRIQUES J F, CASEIRO R, MARTINS P, et al.Exploiting the circulant structure of tracking-by-detection with kernels[C]//Proceedings of European Conference on Computer Vision.Berlin, Germany:Springer, 2012:702-715.
[7] HENRIQUES J F, CASEIRO R, MARTINS P, et al.High-speed tracking with kernelized correlation filters[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(3):583-596.
[8] LI Y, ZHU J.A scale adaptive kernel correlation filter tracker with feature integration[C]//Proceedings of European Conference on Computer Vision.Berlin, Germany:Springer, 2014:254-265.
[9] DANELLJAN M, HÄGER G, KHAN F, et al.Accurate scale estimation for robust visual tracking[C]//Proceedings of British Machine Vision Conference.Nottingham, UK:[s.n], 2014:1-11.
[10] DANELLJAN M, HAGER G, SHAHBAZ KHAN F, et al.Learning spatially regularized correlation filters for visual tracking[C]//Proceedings of IEEE International Conference on Computer Vision.Washington D.C., USA:IEEE Press, 2015:4310-4318.
[11] GALOOGAHI H K, FAGG A, LUCEY S.Learning background-aware correlation filters for visual tracking[C]//Proceedings of IEEE International Conference on Computer Vision.Washingston D.C., USA:IEEE Press, 2017:1135-1143.
[12] LI F, TIAN C, ZUO W, et al.Learning spatial-temporal regularized correlation filters for visual tracking[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2018:4904-4913.
[13] KRIZHEVSKY A, SUTSKEVER I, HINTON G E.Imagenet classification with deep convolutional neural networks[J].Communications of the ACM, 2017, 60(6):84-90.
[14] BERTINETTO L, VALMADRE J, HENRIQUES J F, et al.Fully-convolutional siamese networks for object tracking[C]//Proceedings of European Conference on Computer Vision.Berlin, Germany:Springer, 2016:850-865.
[15] LI B, YAN J, WU W, et al.High performance visual tracking with siamese region proposal network[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2018:8971-8980.
[16] ZHU Z, WANG Q, LI B, et al.Distractor-aware siamese networks for visual object tracking[C]//Proceedings of European Conference on Computer Vision.Berlin, Germany:Springer, 2018:101-117.
[17] ZHANG Z, PENG H.Deeper and wider siamese networks for real-time visual tracking[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2019:4591-4600.
[18] HE K, ZHANG X, REN S, et al.Deep residual learning for image recognition[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2016:770-778.
[19] SIMONYAN K, ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[EB/OL].[2020-10-15].http://arxiv.org/pdf/1409.1556.pdf.
[20] XIE S, GIRSHICK R, DOLLÁR P, et al.Aggregated residual transformations for deep neural networks[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2016:1492-1500.
[21] LI B, WU W, WANG Q, et al.Siamrpn++:evolution of siamese visual tracking with very deep networks[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2019:4282-4291.
[22] FAN H, LING H.Siamese cascaded region proposal networks for real-time visual tracking[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2019:7952-7961.
[23] RUSSAKOVSKY O, DENG J, SU H, et al.Imagenet large scale visual recognition challenge[J].International Journal of Computer Vision, 2015, 115(3):211-252.
[24] HU H, GU J, ZHANG Z, et al.Relation networks for object detection[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2018:3588-3597.
[25] HU J, SHEN L, SUN G.Squeeze-and-excitation networks[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.2018:7132-7141.
[26] WANG X, GIRSHICK R, GUPTA A, et al.Non-local neural networks[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2018:7794-7803.
[27] VASWANI A, SHAZEER N, PARMAR N, et al.Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems.New York, USA:ACM Press, 2017:5998-6008.
[28] CAO Y, XU J, LIN S, et al.GCNet:non-local networks meet squeeze-excitation networks and beyon[C]//Proceedings of IEEE/CVF International Conference on Computer Vision Workshop.Washington D.C., USA:IEEE Press, 2019:1-11.
[29] HOU Q, ZHANG L, CHENG M M, et al.Strip pooling:rethinking spatial pooling for scene parsing[EB/OL].[2020-10-16].https://arxiv.org/abs/2003.13328v1.
[30] REZATOFIGHI H, TSOI N, GWAK J Y, et al.Generalized intersection over union:a metric and a loss for bounding box regression[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2019:658-666.
[31] ZHENG Z, WANG P, LIU W, et al.Distance-iou loss:faster and better learning for bounding box regression[EB/OL].[2020-10-15].https://arxiv.org/abs/1911.08287.
[32] IOFFE S, SZEGEDY C.Batch normalization:accelerating deep network training by reducing internal covariate shift[EB/OL].[2020-10-10].https://arxiv.org/abs/1502.03167.
[33] REAL E, SHLENS J, MAZZOCCHI S, et al.Youtube-boundingboxes:a large high-precision human-annotated data set for object detection in video[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2017:5296-5305.
[34] MUELLER M, SMITH N, GHANEM B.A benchmark and simulator for uav tracking[C]//Proceedings of European Conference on Computer Vision.Berlin, Germany:Springer, 2016:445-461.
[35] Jia X, Lu H, Yang M H.Visual tracking via adaptive structural local sparse appearance model[C]//Proceedings of 2012 IEEE Conference on computer vision and pattern recognition.Washington D.C., USA:IEEE Press, 2012:1822-1829.
[36] HONG Z, CHEN Z, WANG C, et al.Multi-store tracker (muster):a cognitive psychology inspired approach to object tracking[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2015:749-758.
[37] ROSS D A, LIM J, LIN R S, et al.Incremental learning for robust visual tracking[J].International Journal of Computer Vision, 2008, 77(1-3):125-141.

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