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2018 | OriginalPaper | Chapter

Visual Tracking via Spatially Aligned Correlation Filters Network

Authors : Mengdan Zhang, Qiang Wang, Junliang Xing, Jin Gao, Peixi Peng, Weiming Hu, Steve Maybank

Published in: Computer Vision – ECCV 2018

Publisher: Springer International Publishing

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Abstract

Correlation filters based trackers rely on a periodic assumption of the search sample to efficiently distinguish the target from the background. This assumption however yields undesired boundary effects and restricts aspect ratios of search samples. To handle these issues, an end-to-end deep architecture is proposed to incorporate geometric transformations into a correlation filters based network. This architecture introduces a novel spatial alignment module, which provides continuous feedback for transforming the target from the border to the center with a normalized aspect ratio. It enables correlation filters to work on well-aligned samples for better tracking. The whole architecture not only learns a generic relationship between object geometric transformations and object appearances, but also learns robust representations coupled to correlation filters in case of various geometric transformations. This lightweight architecture permits real-time speed. Experiments show our tracker effectively handles boundary effects and aspect ratio variations, achieving state-of-the-art tracking results on recent benchmarks.

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Andriluka, M., Roth, S., Schiele, B.: People-tracking-by-detection and people-detection-by-tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)

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_56CrossRef

Bolme, D., Beveridge, J., Draper, B., Lui, Y.: Visual object tracking using adaptive correlation filters. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2544–2550 (2010)

Chen, D., Hua, G., Wen, F., Sun, J.: Supervised transformer network for efficient face detection. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 122–138. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46454-1_8CrossRef

Choi, J., Chang, H., Yun, S., Fischer, T., Demiris, Y., Choi, J.: Attentional correlation filter network for adaptive visual tracking, pp. 4828–4837 (2017)

Danelljan, M., Bhat, G., Khan, F., Felsberg, M.: ECO: efficient convolution operators for tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 6931–6939 (2017)

Danelljan, M., Häger, G., Khan, F., Felsberg, M.: Convolutional features for correlation filter based visual tracking. In: Proceedings of IEEE International Conference on Computer Vision Workshops, pp. 58–66 (2015)

Danelljan, M., Häger, G., Khan, F., Felsberg, M.: Accurate scale estimation for robust visual tracking. In: Proceedings of British Machine Vision Conference, pp. 65.1–65.11 (2014)

Danelljan, M., Häger, G., Khan, F., Felsberg, M.: Learning spatially regularized correlation filters for visual tracking. In: Proceedings of IEEE International Conference on Computer Vision, pp. 4310–4318 (2015)

10.

Danelljan, M., Khan, F., Felsberg, M., van de Weijer, J.: Adaptive color attributes for real-time visual tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1090–1097 (2014)

11.

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.) ECCV 2016. LNCS, vol. 9909, pp. 472–488. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46454-1_29CrossRef

12.

Deng, J., Dong, W., Socher, R., Li, L., Li, K., Li, F.: Imagenet: a large-scale hierarchical image database. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255 (2009)

13.

Emami, A., Dadgostar, F., Bigdeli, A., Lovell, B.: Role of spatiotemporal oriented energy features for robust visual tracking in video surveillance. In: Proceedings of International Conference on Advanced Video and Signal-Based Surveillance, pp. 349–354 (2012)

14.

Fang, H., Xie, S., Lu, C.: RMPE: regional multi-person pose estimation. arXiv preprint arXiv:1612.00137 (2016)

15.

Hamed, K., Ashton, F., Simon, L.: Learning background-aware correlation filters for visual tracking. In: Proceedings of IEEE International Conference on Computer Vision, pp. 1144–1152 (2017)

16.

Hamed, K., Terence, S., Simon, L.: Correlation filters with limited boundaries. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 4630–4638 (2015)

17.

Held, D., Thrun, S., Savarese, S.: Learning to track at 100 FPS with deep regression networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 749–765. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_45CrossRef

18.

Henriques, J., Caseiro, R., Martins, P., Batista, J.: High-speed tracking with kernelized correlation filters. IEEE Trans Pattern Anal. Mach. Intell. 37(3), 583–596 (2015)CrossRef

19.

Hong, S., You, T., Kwak, S., Han, B.: Online tracking by learning discriminative saliency map with convolutional neural network. In: Proceedings of International Conference on Machine Learning, pp. 597–606 (2015)

20.

Hong, Z., Chen, Z., Wang, C., Mei, X., Prokhorov, D., Tao, D.: Multi-store tracker (MUSTer): a cognitive psychology inspired approach to object tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 749–758 (2015)

21.

Jaderberg, M., Simonyan, K., Zisserman, A.: Spatial transformer networks. In: Proceedings of Neural Information Processing Systems, pp. 2017–2025 (2015)

22.

Kristan, M., et al.: The visual object tracking VOT2015 challenge results. In: Proceedings of IEEE International Conference on Computer Vision Workshops, pp. 1–23 (2015)

23.

Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional neural networks. In: Proceedings of Neural Information Processing Systems, pp. 1097–1105 (2012)

24.

Kwon, J., Lee, K.: Visual tracking decomposition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1269–1276 (2010)

25.

Li, F., Yao, Y., Li, P., Zhang, D., Zuo, W., Yang, M.: Integrating boundary and center correlation filters for visual tracking with aspect ratio variation. arXiv preprint arXiv:1710.02039 (2017)

26.

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_18CrossRef

27.

Liu, L., Xing, J., Ai, H., Ruan, X.: Hand posture recognition using finger geometric feature. In: Proceedings of IEEE International Conference on Pattern Recognition, pp. 565–568 (2012)

28.

Mueller, M., Neil, S., Bernard, G.: Context-aware correlation filter tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1387–1395 (2017)

29.

Ma, C., Huang, J., Yang, X., Yang, M.: Hierarchical convolutional features for visual tracking. In: Proceedings of IEEE International Conference on Computer Vision, pp. 3074–3082 (2015)

30.

Ma, C., Yang, X., Zhang, C., Yang, M.: Long-term correlation tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 5388–5396 (2015)

31.

Nam, H., Han, B.: Learning multi-domain convolutional neural networks for visual tracking, pp. 4293–4302 (2016)

32.

Qi, Y., et al.: Hedged deep tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 4303–4311 (2016)

33.

Wang, Q., Gao, J., Xing, J., Zhang, M., Hu, W.: DCFNet: discriminant correlation filters network for visual tracking. arXiv preprint arXiv:1704.04057 (2017)

34.

Song, W., Zhu, J., Li, Y., Chen, C.: Image alignment by online robust PCA via stochastic gradient descent. IEEE Trans Circuits Syst. Video Technol. 26(7), 1241–1250 (2016)CrossRef

35.

Song, Y., Ma, C., Gong, L., Zhang, J., Lau, R., Yang, M.: CREST: convolutional residual learning for visual tracking, pp. 2574–2583 (2017)

36.

Tang, M., Feng, J.: Multi-kernel correlation filter for visual tracking. In: Proceedings of IEEE International Conference on Computer Vision, pp. 3038–3046 (2015)

37.

Tao, R., Gavves, E., Smeulders, A.: Siamese instance search for tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1420–1429 (2016)

38.

Valmadre, J., Bertinetto, L., Henriques, J., Vedaldi, A., Torr, P.: End-to-end representation learning for correlation filter based tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 5000–5008 (2017)

39.

Vedaldi, A., Lenc, K.: MatConvNet: convolutional neural networks for Matlab. In: ACM MM (2015)

40.

Wang, L., Ouyang, W., Wang, X., Lu, H.: Visual tracking with fully convolutional networks. In: Proceedings of IEEE International Conference on Computer Vision, pp. 3119–3127 (2015)

41.

Wu, Y., Lim, J., Yang, M.H.: Online object tracking: a benchmark. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2411–2418 (2013)

42.

Wu, Y., Lim, J., Yang, M.: Object tracking benchmark. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1834–1848 (2015)CrossRef

43.

Wu, Y., Shen, B., Ling, H.: Online robust image alignment via iterative convex optimization. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1808–1814 (2012)

44.

Zhang, M., Xing, J., Gao, J., Hu, W.: Robust visual tracking using joint scale-spatial correlation filters. In: Proceedings of IEEE International Conference on Image Processing, pp. 1468–1472 (2015)

45.

Zhu, G., Porikli, F., Li, H.: Tracking randomly moving objects on edge box proposals. arXiv preprint arXiv:1507.08085 (2015)

Title: Visual Tracking via Spatially Aligned Correlation Filters Network
Authors: Mengdan Zhang
Qiang Wang
Junliang Xing
Jin Gao
Peixi Peng
Weiming Hu
Steve Maybank
Publisher: Springer International Publishing
Book: Computer Vision – ECCV 2018
Print ISBN: 978-3-030-01218-2

Electronic ISBN: 978-3-030-01219-9

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-030-01219-9_29

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