Abstract
Siamese network based trackers have achieved remarkable performance on visual object tracking. The target position is determined by the similarity map produced via cross-correlation over features generated from template branch and search branch. The interaction between the template and search branches is essential for achieving high-performance object tracking task, which is neglected in previous works as features of the two branches are computed separately. In this paper, we propose Dual Siamese Channel Attentions Networks, referred as SiamDCA, which exploits the channel attentions to further improve tracking robustness. Firstly, a convolutional version of Squeeze and Excitation Networks (CSENet) is embedded in backbone to explicitly formulate interdependencies between channels to recalibrate channel-wise feature responses adaptively. Meanwhile, we propose a novel Global Channel Enhancement (GCE) module, which is capable of capturing attention weights of each channel in template branch, so as to normalize the channel characteristics in search branch. We experiment on benchmark OTB2015, VOT2016 and UAV123 where our algorithm demonstrates competitive performance versus other state-of-the-art trackers.
Supported by the National Natural Science Foundation of China (No. 61977052).
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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
Cao, Y., Xu, J., Lin, S., Wei, F.: GCNet: non-local networks meet squeeze-excitation networks and beyond. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, October 2019
Danelljan, M., Bhat, G., Khan, F.S., Felsberg, M.: ATOM: accurate tracking by overlap maximization. In: CVPR, pp. 4660–4669, June 2019
Danelljan, M., Bhat, G., Shahbaz Khan, F., Felsberg, M.: ECO: efficient convolution operators for tracking. In: CVPR, pp. 6638–6646, July 2017
Danelljan, M., Hager, G., Shahbaz Khan, F., Felsberg, M.: Learning spatially regularized correlation filters for visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 4310–4318, December 2015
Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015). https://doi.org/10.1007/s11263-015-0816-y
Hadfield, S., Bowden, R., Lebeda, K.: The visual object tracking VOT2016 challenge results. In: ECCV Workshops, vol. 9914, pp. 777–823, October 2016
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778, June 2016
Hu, J., Shen, L., Albanie, S., Sun, G., Wu, E.: Squeeze-and-excitation networks. IEEE Trans. Pattern Anal. Mach. Intell. 99, 7132–7141 (2017)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)
Li, B., Wu, W., Wang, Q., Zhang, F., Xing, J., Yan, J.: SiamRPN++: evolution of Siamese visual tracking with very deep networks. In: CVPR, pp. 4282–4291 (2019)
Li, B., Yan, J., Wu, W., Zhu, Z., Hu, X.: High performance visual tracking with Siamese region proposal network. In: CVPR, pp. 8971–8980, June 2018
Li, Y., Zhu, J.: A scale adaptive kernel correlation filter tracker with feature integration. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014. LNCS, vol. 8926, pp. 254–265. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16181-5_18
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P.: Microsoft COCO: common objects in context. In: ECCV, pp. 740–755 (2014)
Liu, L., Xing, J., Ai, H., Ruan, X.: Hand posture recognition using finger geometric feature. In: ICPR, pp. 565–568 (2013)
Mueller, M., Smith, N., Ghanem, B.: A benchmark and simulator for UAV tracking. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 445–461. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_27
Real, E., Shlens, J., Mazzocchi, S., Pan, X., Vanhoucke, V.: YouTube-BoundingBoxes: a large high-precision human-annotated data set for object detection in video. In: CVPR, pp. 5296–5305, July 2017
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017)
Wang, Q., Zhang, L., Bertinetto, L., Hu, W., Torr, P.H.: Fast online object tracking and segmentation: a unifying approach. In: CVPR, pp. 1328–1338, June 2019
Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: CVPR, pp. 7794–7803, June 2018
Wu, Y., Lim, J., Yang, M.H.: Object tracking benchmark. TPAMI 37(9), 1834–1848 (2015)
Xing, J., Ai, H., Lao, S.: Multiple human tracking based on multi-view upper-body detection and discriminative learning. In: ICPR, pp. 1698–1701 (2010)
Yu, Y., Xiong, Y., Huang, W., Scott, M.R.: Deformable Siamese attention networks for visual object tracking. In: CVPR, pp. 6728–6737, June 2020
Zhu, Z., Wang, Q., Li, B., Wu, W., Yan, J., Hu, W.: Distractor-aware Siamese networks for visual object tracking. In: ECCV, pp. 103–119 (2018)
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Gao, W., Tian, X., Zhang, Y., Jia, N., Yang, T., Jiao, L. (2022). Dual Siamese Channel Attention Networks for Visual Object Tracking. In: Shi, Z., Jin, Y., Zhang, X. (eds) Intelligence Science IV. ICIS 2022. IFIP Advances in Information and Communication Technology, vol 659. Springer, Cham. https://doi.org/10.1007/978-3-031-14903-0_28
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