Abstract
In view of drifting issue in object tracking, the conventions are prone to degenerate due to the inaccuracy in appearance models. In this paper, we propose an active-matting-based visual tracker to give more precise contours in targets. The basic idea is to explore the biological inspired color surface coding theory to refine the original interest point detector, which benefits for robust representation to extract the suitable interior object areas for matting. In order to generate accurate pixel-wise labels of each frame for matting, we have both foreground and background interest points using k-d trees between two successive frames, under the similar geometric constraints from the object silhouette in the previous frame. The resulting tracker achieves performance competitive with the state-of-the-art in different color video sequences, especially under the scenarios with strong illuminations and posture variations, as well as small-scale targets in the long-time sequence.
Similar content being viewed by others
References
Akram, M., Izquierdo, E.: Fast motion estimation for surveillance video compression. Signal Image Video Process. 7(6), 502–516 (2013)
Allili, M.S., Ziou, D.: Active contours for video object tracking using region, boundary and shape information. Signal Image Video Process. 1(2), 101–117 (2007)
Comaniciu, D., Ramesh, V.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 564–577 (2003)
Fan, J., Shen, X.H., Wu, Y.: Scribble tracker: a matting-based approach for robust tracking. Pattern Anal. Mach. Intell. 34(8), 1633–1644 (2012)
Friedman, J.H., Bentley, J.L., Raphael, F.A.: An algorithm for finding best matches in logarithmic expected time. ACM Trans. Math. Softw. 3(3), 209–226 (1977)
Godec, M., Roth, P.M., Bischof, H.: Hough-based tracking of non-rigid objects. In: Proceedings of International Conference on Computer Vision (2012)
He, K.M., Sun, J., Tang, X.O.: Fast matting using large kernel matting laplacian matrices. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2165–2172 (2010)
Henriques, J.F., Caseiro, R., Martins, P.: Exploiting the circulant structure of tracking-by-detection with kernels. In: Proceedings of European Conference on Computer Vision, pp. 702–715 (2012)
Jacob, R.J., Karn, K.S.: Eye tracking in human computer interaction and usability research. Ready to deliver the promises. Mind 2(3), 4–11 (2003)
Lennie, P., John, K., Gary, S.: Chromatic mechanisms in striate cortex of macaque. J. Neurosci. 10(2), 649–669 (1990)
Leonard, J.J., Durrant-Whyte, H.F.: Mobile robot localization by tracking geometric beacons. Proc 1995 Conf Centre Adv Stud Collab Res 7(3), 376–382 (1991)
Levin, A., Dani, L., Yair, W.: A closed-form solution to natural image matting. Pattern Anal. Mach. Intell. 30(21), 228–242 (2008)
Michael, I., Andrew, B.: Condensation conditional density propagation for visual tracking. Int. J. Comput. Vis. 29(1), 5–28 (1998)
Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. Int. J. Comput. Vis. 60(1), 63–86 (2004)
Prez, P., Hue, C., Vermaak, J.: Color-based probabilistic tracking. In: Proceedings of the European Conference on Computer Vision, pp. 661–675 (2002)
Ren, X.F., Malik, J.: Tracking as repeated figure/ground segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)
Ross, D.A., Lim, J., Lin, R.: Incremental learning for robust visual tracking. Int. J. Comput. Vis. 77(1–3), 125–141 (2008)
Shapley, R., Hawken, M.J.: Color in the cortex: single- and double-opponent cells. Vis. Res. 51(7), 701–717 (2011)
Vigo, D.A.R., Shahbaz, K.F., Van, J.: The impact of color on bag-of-words based object recognition. In: Proceedings of 20th International Conference on Pattern Recognition, pp. 1549–1553 (2010)
Wu, Y., Lim, J., Yang, M.H.: Online object tracking: a benchmark. In: Computer Vision and Pattern Recognition, pp. 2411–2418 (2013)
Yang, C.J., Ramani, D., Davis, L.: Efficient mean-shift tracking via a new similarity measure. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 176–183 (2005)
Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. Acm Comput. Surv. (CSUR) 38(4), 13–43 (2006)
Zeng, L., Zhang, S.P., Zhang, J.: Dynamic image mosaic via sift and dynamic programming. In: Machine Vision and Application, pp. 1–12 (2013)
Zhang, J., Barhomi, Y., Serre, T.: A new biologically inspired color image descriptor. In: Proceedings of European Conference on Computer Vision, pp. 312–324 (2012)
Zhang, S.P., Yao, H.X., Sun, X.: Robust visual tracking using an effective appearance model based on sparse coding. ACM Trans. Intell. Syst. Technol. 3(3), 43–50 (2012)
Zhang, S.P., Yao, H.X., Sun, X.: Sparse coding based visual tracking: review and experimental comparison. Pattern Recognit. 46(7), 1772–1788 (2013)
Zhang, S.P., Yao, H.X., Zhou, H.Y.: Robust visual tracking based on online learning sparse representation. Neurocomputing 100, 31–40 (2013)
Zhou, H.Y., Yuan, Y., Shi, C.M.: Object tracking using sift features and mean shift. Comput. Vis. Image Underst. 113(3), 345–352 (2009)
Acknowledgments
The authors would like to thank the editor and the anonymous referees for their valuable comments and suggestions that improved quality of this paper. This work was supported by National Natural Science Foundation of China (Nos. 61273237, 61271121 and 60905005).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Sun, X., Zhang, J., Xie, Z. et al. Active-matting-based object tracking with color cues. SIViP 8 (Suppl 1), 85–94 (2014). https://doi.org/10.1007/s11760-014-0637-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11760-014-0637-4