Abstract
In visual tracking task, accuracy and robustness are critical issues for achieveing remarkable performance. In this paper, we propose a novel dual path network with discriminative meta-filters and hierachical representations to solve these issues. We first design geometrically sensitivity pathway (GESP) and geographical sensitivity pathway (GASP) as two subtasks for target classification and scale estimation. GASP mainly includes powerful discriminative meta-filters to find coarse location of target and GESP can refine region of interests online while adapt the appearance model to the target swiftly. Then, a dual path network is developed in a online and offline framework. Specifically, meta-filters are trained offline in order to gain meta-knowledge of similar tracking scenes. Finally, we present three suggestions on deigning modern tracker. Extensive experiments on VOT2018 datasets verify the superior performance of proposed method compared with other state-of-the-arts, achieving expected average overlap (EAO) of 0.467.
This work is supported in part by National Major Project of China for New Generation of AI (No. 2018AAA0100400), in part by the Natural Science Foundation of China under Grant nos. 61773117, 61876088, the Primary Research & Development Plan of Jiangsu Province - Industry Prospects and Common Key Technologies under Grant No. BE2017157.
F. Xie—He is currently working toward the Master degree in the School of Automation, Southeast University.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Huang, L., Zhao, X., Huang, K.: Got-10k: a large high-diversity benchmark for generic object tracking in the wild. In: IEEE Transactions on Pattern Analysis and Machine Intelligence (2019)
Danelljan, M., Bhat, G., Khan, F.S., Felsberg, M.: Atom: accurate tracking by overlap maximization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4660–4669 (2019)
Henriques, J.F., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 583–596 (2014)
Bolme, D.S., Beveridge, J.R., Draper, B.A., Lui, Y.M.: Visual object tracking using adaptive correlation filters. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2544–2550. IEEE (2010)
Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., Torr, P.H.S.: Fully-convolutional siamese networks for object tracking. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9914, pp. 850–865. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48881-3_56
Li, B., Yan, J., Wu, W., Zhu, Z., Hu, X.: 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)
Zhu, Z., Wang, Q., Li, B., Wu, W., Yan, J., Hu, W.: Distractor-aware siamese networks for visual object tracking. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 101–117 (2018)
Li, B., Wu, W., Wang, Q., Zhang, F., Xing, J., Yan, J.: Siamrpn++: evolution of siamese visual tracking with very deep networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4282–4291 (2019)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
Xu, Y., Wang, Z., Li, Z., Ye, Y., Yu, G.: Siamfc++: towards robust and accurate visual tracking with target estimation guidelines. arXiv preprint arXiv:1911.06188 (2019)
Sun, C., Wang, D., Lu, H., Yang, M.-H.: Correlation tracking via joint discrimination and reliability learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 489–497 (2018)
Valmadre, J., Bertinetto, L., Henriques, J., Vedaldi, A., Torr, P.H.S.: 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)
Bhat, G., Johnander, J., Danelljan, M., Khan, F.S., Felsberg, M.: Unveiling the power of deep tracking. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 483–498 (2018)
Tian, Z., Shen, C., Chen, H., He, T.: FCOS: fully convolutional one-stage object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 9627–9636 (2019)
Jiang, B., Luo, R., Mao, J., Xiao, T., Jiang, Y.: Acquisition of localization confidence for accurate object detection. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 784–799 (2018)
Danelljan, M., Robinson, A., Shahbaz Khan, F., Felsberg, M.: Beyond correlation filters: Learning continuous convolution operators for visual tracking. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) European Conference on Computer Vision, vol. 9909, pp. 472–488. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46454-1_29
Danelljan, M., Robinson, A., Shahbaz Khan, F., Felsberg M.: Eco: efficient convolution operators for tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6638–6646 (2017)
Bhat, G., Danelljan, M., Van Gool, L., Timofte, R.: Learning discriminative model prediction for tracking. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 6182–6191 (2019)
Lukezic, A., Matas, J., Kristan, M.: D3s-a discriminative single shot segmentation tracker. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7133–7142 (2020)
Muller, M., Bibi, A., Giancola, S., Alsubaihi, S., Ghanem, B.: Trackingnet: a large-scale dataset and benchmark for object tracking in the wild. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 300–317 (2018)
Fan, H., et al.: Lasot: a high-quality benchmark for large-scale single object tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5374–5383 (2019)
Lin, T.-Y., et al.: Microsoft coco: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) European Conference on Computer Vision, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Xu, N., et al.: Youtube-vos: sequence-to-sequence video object segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 585–601 (2018)
Matej Kristan, et al.: The seventh visual object tracking vot2019 challenge results. In: Proceedings of the IEEE International Conference on Computer Vision Workshops (2019)
Chen, Z., Zhong, B., Li, G., Zhang, S., Ji, R.: Siamese box adaptive network for visual tracking. arXiv preprint arXiv:2003.06761 (2020)
Wang, Q., Zhang, L., Bertinetto, L., Hu, W., Torr, P.H.S.: Fast online object tracking and segmentation: a unifying approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1328–1338 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Xie, F., Wang, N., Yao, Y., Yang, W., Zhang, K., Liu, B. (2020). Hierarchical Representations with Discriminative Meta-filters in Dual Path Network for Tracking. In: Peng, Y., et al. Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science(), vol 12306. Springer, Cham. https://doi.org/10.1007/978-3-030-60639-8_26
Download citation
DOI: https://doi.org/10.1007/978-3-030-60639-8_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60638-1
Online ISBN: 978-3-030-60639-8
eBook Packages: Computer ScienceComputer Science (R0)