Covariance Tracking Algorithm on Bilateral Filtering under Lie Group Structure

Abstract:

The existing object tracking method using covariance modeling is hard to reach the desired tracking performance when the deformation of moving target and illumination changes are drastic, we proposed a object tracking algorithm based on bilateral filtering. Firstly, the algorithm deals the image to be tracked with bilateral filtering, and extracts the needed features of filtered image to construct covariance matrix as tracking model. Secondly, under log-Euclidean Riemannian metric, we construct similarity measure for object covariance matrix and model updating strategy. Extensive experiments show that the proposed method has better adaptability for object deformation and illumination changes.

• Periodical: Applied Mechanics and Materials (Volumes 519-520)
• Pages: 684-688
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.519-520.684
• Online since: February 2014
• Authors: Ying Hong Xie*, Cheng Dong Wu
• Keywords: Bilateral Filtering, Log-Euclidean, Object Tracking

References

[1] LI G W, LIU Y P, YIN J , Target tracking with feature covariance based on an improved Lie group structure,. Chinese Journal of Scientific Instrument, vol. 31, no. 1, pp.111-116, (2010).

[2] WU Y, WANG J Q, Real-time visual tracking via incremental covariance model update on Log-Euclidean Riemannian manifold, Chinese Conference on Pattern Recognition, pp.1-5, (2009).

DOI: 10.1109/ccpr.2009.5344069

[3] GU Q Q, ZHOU J, A similarity measure under Log-euclidean metric for stereo matching, 19th International Conference on Pattern Recognition, pp.1-4, (2008).

DOI: 10.1109/icpr.2008.4761347

[4] DONOSER M, KLUCKNER S, Object tracking by structure tensor analysis,  20th International Conference on  Pattern Recognition, pp.2600-2603, (2010).

DOI: 10.1109/icpr.2010.637

[5] WEN J, GAO XB. Incremental Learning of weighted tensor subspace for visual tracking, Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics, pp.3688-3693, (2009).

DOI: 10.1109/icsmc.2009.5346874

[6] LI L, YAN H, Cost aggregation strategy with bilateral filter based on multi-scale nonlinear structure tensor, Journal of Networks, vol, 6, no. 7, pp.958-965, (2011).

DOI: 10.4304/jnw.6.7.958-965

[7] WANG S H, YOU H J, BFSIFT: A novel method to find feature matches for SAR image registration, IEEE Geoscience and Remote Sensing Letters, vol. 9, no. 4, pp.649-653, (2012).

DOI: 10.1109/lgrs.2011.2177437

[8] LI X, HU W M, Visual tracking via incremental Log-Euclidean Riemannian subspace learning, IEEE Conference on  Computer Vision and Pattern Recognition, pp.1-8, (2008).

DOI: 10.1109/cvpr.2008.4587516