Computer Science ›› 2015, Vol. 42 ›› Issue (9): 278-281.doi: 10.11896/j.issn.1002-137X.2015.09.054

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Visual Object Tracking Algorithm Based on Region Covariance Matrix and 2DPCA Learning

ZHANG Huan-long, ZHENG Wei-dong, SHU Yun-xing and JIANG Bin   

  • Online:2018-11-14 Published:2018-11-14

Abstract: Against the information loss problem during the process of transferring image into vector using PCA in visual tracking,a new adaptive object tracking method based on 2DPCA learning was proposed.It takes the tracked object as a matrix,which can maintain the spatial structure information of the target.And in the particle filter framework,the algorithm adopts the affine model to describe object motion.Meanwhile,in order to enhance the learning ability,the algorithm uses covariance feature fusion to estimate the motion states of the tracked object so as to obtain the robust tracking results.Experimental results indicate that the proposed method achieves favorable performance when the object undergoes illumination changes,pose changes,and partial occlusion between consecutive frames.

Key words: 2DPCA tracking,Affine transformation,Covariance feature fusion,Feature basis learning

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