Abstract
Correlation filters (CFs) have shown excellent performance in visual tracking. They exploit the circular shift of the foreground target to learn the training samples. However, the boundary effect caused by circulant assumption on a small search region limits the ability of such trackers. To overcome this problem, we propose a novel correlation filter tracking framework that takes a larger spatial context into account. In order to make the filter model generate a strong response to the foreground target but a low response to background region, we design an effective cosine window. Extensive experimental results in both quantitative and qualitative measures demonstrate the effectiveness of the proposed algorithm compared to several state-of-the-art trackers.
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
References
Bolme, D.S., Beveridge, J.R., Draper, B.A., et al.: Visual object tracking using adaptive correlation filters. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2544–2550. IEEE (2010)
Danelljan, M., Häger, G., Khan, F., et al.: Accurate scale estimation for robust visual tracking. In: British Machine Vision Conference, Nottingham, 1–5 September 2014. BMVA Press (2014)
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 (2015)
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 (2012)
Liu, S., Zhang, T., Cao, X., et al.: Structural correlation filter for robust visual tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4312–4320 (2016)
Liu, T., Wang, G., Yang, Q.: Real-time part-based visual tracking via adaptive correlation filters. In: Intelligence, p. 2345390 (2015)
Li, Y., Zhu, J., Hoi, S.C.H.: Reliable patch trackers: robust visual tracking by exploiting reliable patches. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 353–361. IEEE (2015)
Lukezic, A., VojÃr, T., Zajc, L.C., et al.: Discriminative correlation filter with channel and spatial reliability. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4847–4856 (2017)
Ma, C., Yang, X., Zhang, C., et al.: Long-term correlation tracking. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5388–5396. IEEE (2015)
Choi, J., Chang, H.J., Yun, S., et al.: Attentional correlation filter network for adaptive visual tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, p. 2 (2017)
Boddeti, V.N., Kanade, T., Kumar, B.V.K.V.: Correlation filters for object alignment. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2291–2298. IEEE (2013)
Dinh, T.B., Vo, N., Medioni, G.: Context tracker: exploring supporters and distracters in unconstrained environments. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1177–1184. IEEE (2011)
Wen, L., Cai, Z., Lei, Z., et al.: Online spatio-temporal structural context learning for visual tracking. In: European Conference on Computer Vision, pp. 716–729. Springer, Berlin (2012)
Zhang, K., Zhang, L., Liu, Q., et al.: Fast visual tracking via dense spatio-temporal context learning. In: European Conference on Computer Vision, pp. 127–141. Springer, Cham (2014)
Mueller, M., Smith, N., Ghanem, B.: Context-aware correlation filter tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1396–1404 (2017)
Galoogahi, H.K., Fagg, A., Lucey, S.: Learning background-aware correlation filters for visual tracking. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 21–26, Honolulu (2017)
Hare, S., Golodetz, S., Saffari, A., et al.: Struck: structured output tracking with kernels. IEEE Trans. Pattern Anal. Mach. Intell. 38(10), 2096–2109 (2016)
Zhong, W., Lu, H., Yang, M.H.: Robust object tracking via sparsity-based collaborative model. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1838–1845. IEEE (2012)
Wu, Y., Lim, J., Yang, M.H.: Online object tracking: a benchmark. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2411–2418. IEEE (2013)
Babenko, B., Yang, M.H., Belongie, S.: Robust object tracking with online multiple instance learning. IEEE Trans. Pattern Anal. Mach. Intell. 33(8), 1619–1632 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Tang, F. et al. (2019). Spatial Context for Correlation Filter Tracking. In: Abawajy, J., Choo, KK., Islam, R., Xu, Z., Atiquzzaman, M. (eds) International Conference on Applications and Techniques in Cyber Security and Intelligence ATCI 2018. ATCI 2018. Advances in Intelligent Systems and Computing, vol 842. Springer, Cham. https://doi.org/10.1007/978-3-319-98776-7_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-98776-7_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-98775-0
Online ISBN: 978-3-319-98776-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)