• Laser & Optoelectronics Progress
  • Vol. 57, Issue 2, 21510 (2020)
Dong Jifu1, Liu Chang1、*, Cao Fangwei1, Ling Yuan2, and Gao Xiang1
Author Affiliations
  • 1College of Information Sciences and Technology, Dalian Maritime University, Dalian, Liaoning 116026, China
  • 2Hualu Zhida Technology Co., Ltd., Dalian, Liaoning 116023, China
  • show less
    DOI: 10.3788/LOP57.021510 Cite this Article Set citation alerts
    Dong Jifu, Liu Chang, Cao Fangwei, Ling Yuan, Gao Xiang. Online Adaptive Siamese Network Tracking Algorithm Based on Attention Mechanism[J]. Laser & Optoelectronics Progress, 2020, 57(2): 21510 Copy Citation Text show less
    References

    [1] Li P, Zhang Y. Video smoke detection based on Gaussian mixture model and convolutional neural network[J]. Laser & Optoelectronics Progress, 56, 211502(2019).

    [2] Li Z R, Wang K X, He X L et al. Heel-strike event detection algorithm based on convolutional neural networks[J]. Laser & Optoelectronics Progress, 56, 211503(2019).

    [3] Wang D C, Chen X N, Zhao F et al. Vehicle detection algorithm based on convolutional neural network and RGB-D images[J]. Laser & Optoelectronics Progress, 56, 181003(2019).

    [4] Nam H, Han B. Learning multi-domain convolutional neural networks for visual tracking. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 4293-4302(2016).

    [5] Song Y B, Ma C, Gong L J et al. CREST: convolutional residual learning for visual tracking. [C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy. New York: IEEE, 2574-2583(2017).

    [6] Wang Q, Teng Z, Xing J L et al. Learning attentions: residual attentional Siamese network for high performance online visual tracking. [C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 4854-4863(2018).

    [7] Chu Q, Ouyang W L, Li H S et al. Online multi-object tracking using CNN-based single object tracker with spatial-temporal attention mechanism. [C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy. New York: IEEE, 4846-4855(2017).

    [8] Hu J, Shen L, Sun G. Squeeze-and-excitation networks. [C]∥IEEE Conference on Computer Vision and Pattern Recognition(CVPR), June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 7132-7141(2018).

    [9] Yang D W, Gong X F, Mao L et al. Multi-domain convolutional neural network tracking algorithm based on reconstructed feature combination[J]. Laser & Optoelectronics Progress, 56, 191501(2019).

    [10] Bertinetto L, Valmadre J, Henriques J F et al. Fully-convolutional Siamese networks for object tracking[M]. ∥Hua G, Jégou H. Computer vision-ECCV 2016 workshops. Lecture notes in computer science. Cham: Springer, 9914, 850-865(2016).

    [11] Held D, Thrun S, Savarese S. Learning to track at 100 FPS with deep regression networks[M]. //Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science. Cham: Springer, 9905, 749-765(2016).

    [12] Fan H, Ling H B. SANet: structure-aware network for visual tracking. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2217-2224(2017).

    [13] He A F, Luo C, Tian X M et al. A twofold Siamese network for real-time object tracking. [C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 4834-4843(2018).

    [14] Zhang Y H, Wang L J, Qi J Q et al. Structured Siamese network for real-time visual tracking[M]. ∥Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science. Cham: Springer, 11213, 355-370(2018).

    [15] KashianiH, Shokouhi S B. Visual object tracking based on adaptive Siamese and motion estimation network[J].Image and VisionComputing, 2019, 83/84: 17- 28.

    [16] Li L X. Research on visual tracking based on deep learning[D]. Harbin: Harbin Institute of Technology, 31-32(2018).

    [17] Zeiler M D, Fergus R. Visualizing and understanding convolutional networks[M]. ∥Fleet D, Pajdla T, Schiele B, et al. Computer vision-ECCV 2014. Lecture notes in computer science. Cham: Springer, 8689, 818-833(2014).

    [18] Wu Y, Lim J, Yang M H. Online object tracking: a benchmark. [C]∥2013 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2013, Portland, OR, USA. New York: IEEE, 2411-2418(2013).

    [19] Wu Y, Lim J, Yang M H. Object tracking benchmark[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 1834-1848(2015).

    [20] Kristan M, Leonardis A, Matas J et al. The visual object tracking vot2016 challenge results[M]. ∥Hua G, Jégou H. Computer vision-ECCV 2016 Workshops. Lecture notes in computer science. Cham: Springer, 9914, 777-823(2016).

    [21] Bertinetto L, Valmadre J, Golodetz S et al. Staple: complementary learners for real-time tracking. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 1401-1409(2016).

    [22] Danelljan M, Hager G, Khan F S et al. Convolutional features for correlation filter based visual tracking. [C]∥2015 IEEE International Conference on Computer Vision Workshop (ICCVW), December 7-13, 2015, Santiago, Chile. New York: IEEE, 621-629(2015).

    [23] 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, 37, 583-596(2015).

    [24] Russakovsky O, Deng J, Su H et al. ImageNet large scale visual recognition challenge[J]. International Journal of Computer Vision, 115, 211-252(2015).

    [25] Vedaldi A, Lenc K. MatConvNet: convolutional neural networks for MATLAB. [C]∥Proceedings of the 23rd ACM international conference on Multimedia-MM'15, October 26-30, 2015, Brisbane, Australia. New York: ACM, 689-692(2015).

    [26] Babenko B, Yang M H, Belongie S. Robust object tracking with online multiple instance learning[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33, 1619-1632(2011).

    Dong Jifu, Liu Chang, Cao Fangwei, Ling Yuan, Gao Xiang. Online Adaptive Siamese Network Tracking Algorithm Based on Attention Mechanism[J]. Laser & Optoelectronics Progress, 2020, 57(2): 21510
    Download Citation