SCIA 2013: Image Analysis pp 652-663 | Cite as
Robust Scale-Adaptive Mean-Shift for Tracking
Abstract
Mean-Shift tracking is a popular algorithm for object tracking since it is easy to implement and it is fast and robust. In this paper, we address the problem of scale adaptation of the Hellinger distance based Mean-Shift tracker.
We start from a theoretical derivation of scale estimation in the Mean-Shift framework. To make the scale estimation robust and suitable for tracking, we introduce regularization terms that counter two major problem: (i) scale expansion caused by background clutter and (ii) scale implosion on self-similar objects. To further robustify the scale estimate, it is validated by a forward-backward consistency check.
The proposed Mean-shift tracker with scale selection is compared with recent state-of-the-art algorithms on a dataset of 48 public color sequences and it achieved excellent results.
Keywords
object tracking mean-shift scale estimationReferences
- 1.Bradski, G.R.: Computer Vision Face Tracking For Use in a Perceptual User Interface. Intel Technology Journal Q2 (1998)Google Scholar
- 2.Collins, R.T.: Mean-shift blob tracking through scale space. In: Computer Vision and Pattern Recognition, pp. 234–240. IEEE Computer Society (2003)Google Scholar
- 3.Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non-rigid objects using mean shift, pp. 142–149 (2000)Google Scholar
- 4.Fukunaga, K., Hostetler, L.: The estimation of the gradient of a density function, with applications in pattern recognition. Information Theory 21(1), 32–40 (1975)MathSciNetMATHCrossRefGoogle Scholar
- 5.Hare, S., Saffari, A., Torr, P.: Struck: Structured output tracking with kernels. In: International Conference Computer Vision, pp. 263–270 (November 2011)Google Scholar
- 6.Kalal, Z., Matas, J., Mikolajczyk, K.: P-N Learning: Bootstrapping Binary Classifiers by Structural Constraints. In: Conference on Computer Vision and Pattern Recognition (2010)Google Scholar
- 7.Klein, D.A., Schulz, D., Frintrop, S., Cremers, A.B.: Adaptive real-time video-tracking for arbitrary objects. In: Intelligent Robots and Systems, pp. 772–777 (October 2010)Google Scholar
- 8.Liang, D., Huang, Q., Jiang, S., Yao, H., Gao, W.: Mean-shift blob tracking with adaptive feature selection and scale adaptation. In: International Conference Image Processing (2007)Google Scholar
- 9.Ning, J., Zhang, L., Zhang, D., Wu, C.: Robust mean-shift tracking with corrected background-weighted histogram. Computer Vision, IET 6(1), 62–69 (2012)MathSciNetCrossRefGoogle Scholar
- 10.Ning, J., Zhang, L., Zhang, D., Wu, C.: Scale and orientation adaptive mean shift tracking. Computer Vision, IET 6(1), 52–61 (2012)MathSciNetCrossRefGoogle Scholar
- 11.Ning, J., Zhang, L., Zhang, D., Wu, C.: Scale and orientation adaptive mean shift tracking. Computer Vision, IET 6(1), 52–61 (2012)MathSciNetCrossRefGoogle Scholar
- 12.Pu, J.-X., Peng, N.-S.: Adaptive kernel based tracking using mean-shift. In: Campilho, A., Kamel, M.S. (eds.) ICIAR 2006. LNCS, vol. 4141, pp. 394–403. Springer, Heidelberg (2006)CrossRefGoogle Scholar
- 13.Čehovin, L., Kristan, M., Leonardis, A.: An adaptive coupled-layer visual model for robust visual tracking. In: 13th International Conference on Computer Vision (November 2011)Google Scholar
- 14.Yang, C., Duraiswami, R., Davis, L.: Efficient mean-shift tracking via a new similarity measure. In: Computer Vision and Pattern Recognition, vol. 1, pp. 176–183 (June 2005)Google Scholar
- 15.Zhang, K., Zhang, L., Yang, M.-H.: Real-time compressive tracking. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part III. LNCS, vol. 7574, pp. 864–877. Springer, Heidelberg (2012)CrossRefGoogle Scholar
- 16.Zhao, C., Knight, A., Reid, I.: Target tracking using mean-shift and affine structure. In: ICPR, pp. 1–5 (2008)Google Scholar