Abstract
In order to improve accuracy and robustness of object tracking, and to resolve the problem that kernel circulant object tracking is easily affected by illumination changes, this paper presents a kernel circulant object tracking method based on illumination invariant features. Firstly, the locality sensitive histograms of input image are calculated and illumination invariant features are extracted. Then, the precise position of tracking object is obtained by the responded confidence image, which can be quickly computed in frequency domain between input image and template image. Tests of many video sequences demonstrate that the proposed method can track the target accurately and effectively. Comparing to the conditional kernel circulant tracking algorithm, the tracking error using our method decreases 24 pixels and the tracking precision increases 28%. As a result, the proposed algorithm has the better robustness under abruptly variant illumination and pose.
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
Wang, X., Hua, G., Han, T.X.: Discriminative tracking by metric learning. In: Proceedings of European Conference on Computer Vision, Crete, Greece, pp. 200–214 (2010)
Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking learning detection. IEEE Trans. Pattern Anal. Mach. Intell. 34, 1409–1422 (2012)
Avida, S.: Ensemble tracking. IEEE Trans. Pattern Anal. Mach. Intell. 29(2), 261–271 (2007)
Zhang, K., Zhang, L., Yang, M.H.: Real-time compressive tracking. In: ECCV, pp. 866–879 (2012)
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)
Henriques, J.F., Caseiro, R., Martins, P., et al.: Exploiting the circulant structure of tracking-by-detection with kernels. In: ECCV, pp. 702–715 (2012)
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)
Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. ACM Comput. Surv. 38(4), 13–58 (2006)
He, S.F., Yang, Q.X., Rynson, W.H.L., et al.: Visual tracking via locality sensitive histograms. In: CVPR, pp. 2427–2434 (2013)
Sun, X.Y., Chang, F.L.: Object tracking based on sparse representation of gradient feature. Opt. Precis. Eng. 21(12), 3191–3197 (2013)
Tian, H., Ju, Y.F., Meng, F.K., Li, F.F.: Improved tracking algorithm with background-weighted histogram. J. Image Graph. 20(1), 0072–0084 (2015)
Adam, A., Rivlin, E., Shimshoni, I.: Robust fragments-based tracking using the integral histogram. In: CVPR, pp. 798–805 (2006)
Acknowledgments
This research was supported by National Natural Science Foundation of China (No. 61771230).
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
Wang, Z., Xu, B., Zhang, X. (2020). Kernel Circulant Object Tracking Based on Illumination Invariant Features. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1074. Springer, Cham. https://doi.org/10.1007/978-3-030-32456-8_94
Download citation
DOI: https://doi.org/10.1007/978-3-030-32456-8_94
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32455-1
Online ISBN: 978-3-030-32456-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)