Moving Target Tracking Algorithm Based on Detail Extraction

doi:10.15888/j.cnki.csa.007242

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Moving Target Tracking Algorithm Based on Detail Extraction
DOI:
                        10.15888/j.cnki.csa.007242
                    
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                        LI KeLI Ke
College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China
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CAI Jian-YongCAI Jian-Yong
College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China;Key Laboratory of Optoelectronic Science and Technology for Medicine (Ministry of Education), Fujian Normal University, Fuzhou 350007, China;Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou 350007, China;Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fujian Normal University, Fuzhou 350007, China
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ZHANG Ming-WeiZHANG Ming-Wei
College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China
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LU Yi-HongLU Yi-Hong
College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China
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ZENG Yuan-QiangZENG Yuan-Qiang
College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China
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Abstract:

At present, the interference in the moving target tracking task is very deceptive, and the target tracking algorithm is easily deceived by the data set with traps. In order to improve the tracking algorithm's effect on tracking dataset, this study proposes an improved DPP-SiamFC tracking algorithm based on SiamFC twinning network. This algorithm introduces DPP (Detail-Perserving Pooling) pooling layer and residual network based on the original network, effectively retaining the details of the target. This study also verifies network performance on the VOT2017 tracking dataset, the experimental results have achieved the goal of improving network performance.

Key words:DPP pooling layer|DPP-SiamFC|residual network|multitasking|details of the target

Reference

[1] Hua Y, Alahari K, Schmid C. Online object tracking with proposal selection. Proceedings of 2015 IEEE International Conference on Computer Vision. Santiago, Chile. 2015. 3092-3100.

[2] Zhu G, Porikli F, Li HD. Beyond local search:Tracking objects everywhere with instance-specific proposals. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA. 2016. 943-951.

[3] Yang TY, Chan AB. Recurrent filter learning for visual tracking. Proceedings of 2017 IEEE International Conference on Computer Vision Workshops. Venice, Italy. 2017. 2010-2019.

[4] Kosiorek AR, Bewley A, Posner I. Hierarchical attentive recurrent tracking. arXiv preprint arXiv:1706.09262, 2017.

[5] He AF, Luo C, Tian XM, et al. A twofold Siamese network for real-time object tracking. Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, USA. 2018. 4834-4843.

[6] Hao ZX, Li Y, You SD, et al. Detail preserving depth estimation from a single image using attention guided networks. Proceedings of 2018 International Conference on 3D Vision. Verona, Italy. 2018. 304-313.

[7] Zhu G, Porikli F, Li HD. Tracking randomly moving objects on edge box proposals. arXiv:1507.08085, 2015.

[8] Kristan M, Matas J, Leonardis A, et al. The visual object tracking vot2015 challenge results. Proceedings of the IEEE International Conference on Computer Vision Workshops. Santiago, Chile. 2015. 564-586.

[9] Weber N, Waechter M, Amend SC, et al. Rapid, detail-preserving image downscaling. ACM Transactions on Graphics, 2016, 35(6):205

[10] Choi J, Chang HJ, Jeong J, et al. Visual tracking using attention-modulated disintegration and integration. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA. 2016. 4321-4330.

[11] Bertinetto L, Valmadre J, Henriques JF, et al. Fully-convolutional Siamese networks for object tracking. Proceedings of European Conference on Computer Vision. Amsterdam, The Netherlands. 2016. 850-865.

[12] Wang MM, Liu Y, Huang ZY. Large margin object tracking with circulant feature maps. Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, USA. 2017. 4800-4808.

[13] Huang C, Lucey S, Ramanan D. Learning policies for adaptive tracking with deep feature cascades. Proceedings of 2017 IEEE International Conference on Computer Vision. Venice, Italy. 2017. 105-114.

[14] Hu J, Shen L, Sun G. Squeeze-and-excitation networks. Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, USA. 2018. 7132-7141.

[15] Wang LJ, Ouyang WL, Wang XG, et al. Visual tracking with fully convolutional networks. Proceedings of 2015 IEEE International Conference on Computer Vision. Santiago, Chile. 2015. 3119-3127.

[16] Wu Y, Lim J, Yang MH. Object tracking benchmark. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9):1834-1848. doi:10.1109/TPAMI.2014.2388226

[17] Tao R, Gavves E, Smeulders AWM. Siamese instance search for tracking. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA. 2016. 1420-1429.

[18] Cen MB, Jung C. Fully convolutional Siamese fusion networks for object tracking. Proceedings of 2018 25th IEEE International Conference on Image Processing. Athens, Greece. 2018. 3718-3722.

[19] Liang PP, Blasch E, Ling HB. Encoding color information for visual tracking:Algorithms and benchmark. IEEE Transactions on Image Processing, 2015, 24(12):5630-5644. doi:10.1109/TIP.2015.2482905

[20] Danelljan M, Häger G, Khan FS, et al. Convolutional features for correlation filter based visual tracking. Proceedings of the IEEE International Conference on Computer Vision Workshops. Santiago, Chile. 2015. 621-629.

[21] Abadi M, Agarwal A, Barham P, et al. Tensorflow:Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467, 2016.

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李科,蔡坚勇,张明伟,卢依宏,曾远强.基于细节提取的运动目标追踪算法.计算机系统应用,2020,29(1):184-189

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History

Received:June 19,2019
Revised:July 16,2019
Adopted:
Online: December 30,2019
Published: January 15,2020

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