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SiamCAR-Kal: anti-occlusion tracking algorithm for infrared ground targets based on SiamCAR and Kalman filter

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Abstract

During infrared ground target tracking, to ensure accurate tracking and minimize target loss caused by background occlusion, an anti-occlusion tracking algorithm based on SiamCAR and Kalman filtering, namely SiamCAR-Kal algorithm, is proposed. First, the algorithm uses the response of the classification branch to determine the occlusion state, and then, to address the short-term total occlusion problem, it uses the Kalman filter algorithm to determine the target position based on historical information. To address the problem of low accuracy of long-term total occlusion of the Kalman filter, an extended search strategy is proposed to achieve target recapture. The experimental results show that the proposed algorithm can not only improve the tracking precision, but also effectively solve the problem of target loss caused by occlusion. It is tested on a sequence of infrared ground targets, and compared with the existing typical SiamCAR, the success rate and precision of the proposed algorithm show an improvement of 3.2% and 4.5% under one pass evaluation indexes.

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References

  1. Guo, D., Wang, J., Cui, Y., et al.: SiamCAR: Siamese fully convolutional classification and regression for visual tracking. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6269–6277 (2020)

  2. Bolme, D.S., Beveridge, J.R., Draper, B.A., et al.: Visual object tracking using adaptive correlation filters. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2544–2550. IEEE (2010)

  3. Henriques, J.F., Caseiro, R., Martins, P., et al.: Exploiting the circulant structure of tracking-by-detection with kernels. In: European Conference on Computer Vision, pp. 702–715. Springer, Berlin, Heidelberg, (2012)

  4. Henriques, J.F., Caseiro, R., Martins, P., et al.: High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583–596 (2014)

    Article  Google Scholar 

  5. Bertinetto, L., Valmadre, J., Golodetz, S., et al.: Staple: complementary learners for real-time tracking. IN: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1401–1409 (2016)

  6. Danelljan, M., Robinson, A., Khan, F.S., et al.: Beyond correlation filters: learning continuous convolution operators for visual tracking. In: European Conference on Computer Vision, pp. 472–488. Springer, Cham (2016)

  7. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2014)

  8. Danelljan, M., Bhat, G., Shahbaz, K.F., et al.: Eco: efficient convolution operators for tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6638–6646 (2017)

  9. Bertinetto, L., Valmadre, J., Henriques, J.F., et al.: Fully-convolutional siamese networks for object tracking. In: European Conference on Computer Vision, pp. 850–865. Springer, Cham (2016)

  10. Li, B., Yan, J., Wu, W., et al.: High performance visual tracking with siamese region proposal network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition pp. 8971–8980 (2018)

  11. Ren, S., He, K., Girshick, R., et al.: Faster r-cnn: towards real-time object detection with region proposal networks. Adv. Neural. Inf. Process. Syst. 28, 91–99 (2015)

    Google Scholar 

  12. Li, B., Wu, W., Wang, Q., et al.: Evolution of siamese visual tracking with very deep networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, pp. 16–20 (2019)

  13. He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

  14. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural. Inf. Process. Syst. 25, 1097–1105 (2012)

    Google Scholar 

  15. Wang, Q., Zhang, L., Bertinetto, L., et al.: Fast online object tracking and segmentation: a unifying approach. IN: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1328–1338 (2019)

  16. Chen, Z., Zhong, B., Li, G., et al.: Siamese box adaptive network for visual tracking. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6668–6677 (2020)

  17. Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. IEEE Trans. Pattern Anal. Mach. Intell. 34(7), 1409–1422 (2011)

    Article  Google Scholar 

  18. Zhu, Z., Wang, Q., Li, B., et al.: Distractor-aware siamese networks for visual object tracking. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 101–117 (2018)

  19. Gupta, D.K., Gavves, E., Smeulders, A.W.M.: Tackling occlusion in Siamese tracking with structured dropouts. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 5804–5811. IEEE (2021)

  20. Diao, Z.: A single target tracking algorithm based on Generative Adversarial Networks. arXiv:1912.11967 (2019)

  21. Wang, M., Liu, Y., Huang, Z.: Large margin object tracking with circulant feature maps. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4021–4029 (2017)

  22. Russakovsky, O., Deng, J., Su, H., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)

    Article  MathSciNet  Google Scholar 

  23. Lin, T.Y., Maire, M., Belongie, S., et al.: Microsoft coco: common objects in context. In: European Conference on Computer Vision, pp. 740–755. Springer, Cham (2014)

  24. Real, E., Shlens, J., Mazzocchi, S., et al.: Youtube-boundingboxes: a large high-precision human-annotated data set for object detection in video. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5296–5305 (2017)

  25. Liu, Q., Li, X., He, Z., et al.: LSOTB-TIR: a large-scale high-diversity thermal infrared object tracking benchmark. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 3847–3856 (2020)

  26. Collins, R., Zhou, X., The, S.K.: An open source tracking testbed and evaluation web site. In: IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, Vol. 2, No. 6, pp. 35 (2005)

  27. Valmadre, J., Bertinetto, L., Henriques, J., et al.: End-to-end representation learning for correlation filter based tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2805–2813 (2017)

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Authors and Affiliations

  1. Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an, 710072, China

    Guodong Fu & Kai Zhang

  2. School of Astronautics, Northwestern Polytechnical University, Xi’an, 710072, China

    Xi Yang

  3. North Automatic Control Technology Institute, Taiyuan, 030006, China

    Xiaoqian Tian

  4. School of Information and Communication Engineering, Xi’an Jiaotong University, Xi’an, 710049, China

    XiaoTian Wang

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  1. Guodong Fu
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  2. Kai Zhang
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  4. Xiaoqian Tian
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  5. XiaoTian Wang
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Correspondence to Kai Zhang.

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Fu, G., Zhang, K., Yang, X. et al. SiamCAR-Kal: anti-occlusion tracking algorithm for infrared ground targets based on SiamCAR and Kalman filter. Machine Vision and Applications 34, 43 (2023). https://doi.org/10.1007/s00138-023-01393-3

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