Abstract：A method for object tracking is presented to track objects steadily based on incremental linear discriminant analysis. The locations and poses of the object are represented by a set of affine parameters. Resorting to a state transition model, a group of image samples are predicted as candidates of the image patches of the object in the next frame. Their likelihoods of being the object image patch are measured after they are projected into a linear discriminant subspace. Then a sample with maximum likelihood is regarded as the object image patch. Finally, sufficient spanning sets of total scatter matrix and between-class scatter matrix are rotated to update projection matrix for maintaining the discrimination power of the subspace. Experimental results show that the method is robust to variation in appearances of objects and surrounding background, and it is available in affine invariant tracking.
[1] Avidan S. Support Vector Tracking. IEEE Trans on Pattern Analysis and Machine Intelligence, 2004, 26(8): 1064-1072 [2] Grabner M, Grabner H, Bischof H. Learning Features for Tracking [EB/OL]. [2007-06-28]. http://www.icg.tugraz.at/pub/pdf/iptracking.pdf [3] Avidan S. Ensemble Tracking. IEEE Trans on Pattern Analysis and Machine Intelligence. 2007, 29(2): 261-271 [4] Tuzel O, Porikli F, Meer P. Learning on Lie Groups for Invariant Detection and Tracking [EB/OL]. [2008-06-27]. http://www.caip.rutgers.edu/riul/research/papers/pdf/lietrackdet.pdf [5] Ho J, Lee K C, Yang M H, et al. Visual Tracking Using Learned Linear Subspaces // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA, 2004: 782-789 [6] Lin R S, Yang M H, Levinson S E. Object Tracking Using Incremental Fisher Discriminant Analysis // Proc of the 17th International Conference on Pattern Recognition. Cambridge, UK, 2004, Ⅱ: 757-760 [7] Li Xi, Hu Weiming, Zhang Zhongfei, et al. Visual Tracking via Incremental Log-Euclidean Riemannian Subspace Learning [EB/OL]. [2008-06-27]. http://www.fortune.binghamton.edu/publications/tracking_final.pdf [8] Collins R T, Liu Y, Leordeanu M. Online Selection of Discriminative Tracking Features. IEEE Trans on Pattern Analysis and Machine Intelligence, 2005, 27(10): 1631-1643 [9] Pang Shaoning, Ozawa S, Kasabov N. Incremental Linear Discriminant Analysis for Classification of Data Streams. IEEE Trans on System, Man and Cybernetics, 2005, 35(5): 905-914 [10] Kim T K, Wong S F, Stenger B, et al. Incremental Linear Discriminant Analysis Using Sufficient Spanning Set Approximations [EB/OL]. [2007-06-23]. http://mi.eng.cam.ac.uk/~tkk22/doc/cvpro7_ilda_final.pdf [11] Fukunaga K. Introduction to Statistical Pattern Recognition. Boston, USA: Academic Press, 1990 [12] Comaniciu D, Ramesh V, Meer P. Kernel-Based Object Tracking. IEEE Trans on Pattern Analysis and Machine Intelligence, 2003, 25(5): 564-577
2010, Vol. 23 
