An object tracking method based on weighted incremental subspace learning was proposed to overcome the difficulty in object tracking resulting from variations in appearance. The method constructs a subspace updated online to depict the appearance of the object in the video. A set of image patches are predicted based on probabilistic transformation model as candidate image regions of the object in the current frame, then these image patches are projected onto the low-dimensional subspace, and the likelihood of each image patch as the image region of the object is evaluated. The image patch with maximal likelihood is regarded as the object image region. Finally, the subspace is updated incrementally with temporal weights. Experimental results show that the method accomplishes object tracking more steadily and accurately, compared with other incremental subspace learning based object tracking methods.
［1］ DORIN C, VISVANATHAN R, PETER M. Kernelbased object tracking ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(5): 564-577. ［2］ HANZI W, DAVID S, KONRAD S, et al. Adaptive object tracking based on an effective appearance filter ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(9): 1661-1667. ［3］ ZHOU H, YUAN Y, ZHANG Y, et al. Nonrigid object tracking in complex scenes ［J］. Pattern Recognition Letters, 2009, 30(2): 98-102. ［4］ 袁广林,薛模根,谢恺,等.基于核函数粒子滤波和多特征自适应融合的目标跟踪［J］.计算机辅助设计与图形学学报,2009,21(12): 1774-1784. YUAN Guanglin, XUE Mogen, XIE Kai, et al. Kernelbased particle filter for target tracking with adaptive multiple feature fusion ［J］. Journal of ComputerAided Design and Computer Graphics, 2009, 21(12): 1774-1784. ［5］ 王震宇,张可黛,吴毅,等.基于SVM和Adaboost的红外目标跟踪［J］.中国图象图形学报,2007,12(11): 2052-2057. WANG Zhenyu, ZHANG Kedai, WU Yi. Target tracking in infrared image sequences by combining SVM and Adaboost ［J］. Journal of Image and Graphics, 2007, 12(11): 2052-2057. ［6］ ROBERT T C, LIU Y, MARIUS L. Online selection of discriminative tracking features ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(10): 1631-1643. ［7］ LI Y. On incremental and robust subspace learning ［J］. Pattern Recognition, 2004, 37(7): 1509-1518. ［8］ MICHAEL J B, ALLAN D J. EigenTracking: robust matching and tracking of articulated object using a viewbased representation ［J］. International Journal of Computer Vision, 1998, 26(1): 63-84. ［9］ LEVY A, LINEDENBAUM M. Sequential karhunenloeve basis extraction and its application to images ［J］. IEEE Transactions on Image Processing, 2000, 9(8): 1371-1374. ［10］ DANIJEL S, ALES L. Incremental and robust learning of subspace representations ［J］. Image and Vision Computing, 2008, 26(1): 27-38. ［11］ HO J, LEE K, YANG M, et al. Visual tracking using learned linear subspace ［C］ ∥Proceedings of 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington DC: IEEE, 2004: 782-789. ［12］ DAVID A R, LIM J, LIN R, et al. Incremental learning for robust visual tracking ［J］. International Journal of Computer Vision, 2008, 77(1): 125-141.
