Abstract
This paper focuses on the optimization and improvement of visual-based object tracking algorithm. Reflecting from previously used tracking algorithm, we approach the problem using L2-regularized least squares to solve the sparse representation matrix of the object appearance model and propose an efficient collaborative algorithm to track the object. A hierarchical framework and selective multi-memory based online dictionary update are developed to upgrade the speed of the algorithm and improve the robustness by considering both current and history appearance into the template. In addition, key-point feature matching is novelly proposed to further enhance the accuracy of the tracking algorithm by calculating an optical flow based similarity degree. Finally, the proposed algorithm is verified using comprehensive image sequence datasets to demonstrate its effectiveness on coping with various tracking challenges, such as object deformations, illumination changes and partial occlusions.
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
Smeulders, A.W., Chu, D.M., Cucchiara, R., Calderara, S., Dehghan, A., Shah, M.: Visual tracking: An experimental survey. Pattern Anal. Mach. Intell. IEEE Trans. 36(7), 1442–1468 (2014)
Yilmaz, A., Javed, O., Shah, M.: Object tracking: A survey. ACM Comput. Surv. (CSUR) 38(4), 13 (2006)
Mei, X., Ling, H.: Robust visual tracking using l1 minimization. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 1436–1443. IEEE (2009)
Xiao, Z., Lu, H., Wang, D.: L2-rls-based object tracking. Circ. Syst. Video Technol. IEEE Trans. 24(8), 1301–1309 (2014)
Zhang, K., Zhang, L., Yang, M.-H.: Fast compressive tracking. Pattern Anal. Mach. Intell. IEEE Trans. 36(10), 2002–2015 (2014)
Xing, J., Gao, J., Li, B., Hu, W., Yan, S.: Robust object tracking with online multi-lifespan dictionary learning. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 665–672 (2013)
Bao, C., Wu, Y., Ling, H., Ji, H.: Real time robust l1 tracker using accelerated proximal gradient approach. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1830–1837. IEEE (2012)
Zhong, W., Lu, H., Yang, M.-H.: Robust object tracking via sparse collaborative appearance model. Image Process. IEEE Trans. 23(5), 2356–2368 (2014)
Ross, D.A., Lim, J., Lin, R.-S., Yang, M.-H.: Incremental learning for robust visual tracking. Int. J. Comput. Vis. 77(1–3), 125–141 (2008)
Wang, Q., Chen, F., Yang, J., Xu, W., Yang, M.-H.: Transferring visual prior for online object tracking. Image Process. IEEE Trans. 21(7), 3296–3305 (2012)
Wu, Y., Lim, J., Yang, M.-H.: Object tracking benchmark. Pattern Anal. Mach. Intell. IEEE Trans. 37(9), 1834–1848 (2015)
Acknowledgement
This work is supported by Defence Innovative Research Programme (DIRP), the Ministry of Defence, Singapore under grant R-263-000-B08-592.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Tu, F., Ge, S.S., Suryadi, H.P., Tang, Y., Hang, C.C. (2016). Collaborative Visual Object Tracking via Hierarchical Structure. In: Agah, A., Cabibihan, JJ., Howard, A., Salichs, M., He, H. (eds) Social Robotics. ICSR 2016. Lecture Notes in Computer Science(), vol 9979. Springer, Cham. https://doi.org/10.1007/978-3-319-47437-3_40
Download citation
DOI: https://doi.org/10.1007/978-3-319-47437-3_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47436-6
Online ISBN: 978-3-319-47437-3
eBook Packages: Computer ScienceComputer Science (R0)