Incremental MPCA for Color Object Tracking

The task of visual tracking is to deal with dynamic image streams that change over time. For color object tracking, although a color object is a 3-order tensor in essence, little attention has been focused on this attribute. In this paper, we propose a novel Incremental Multiple Principal Component Analysis (IMPCA) method for online learning dynamic tensor streams. When newly added tensor set arrives, the mean tenor and the covariance matrices of different modes can be updated easily, and then projection matrices can be effectively calculated based on covariance matrices. Finally, we apply our IMPCA method to color object tracking using Bayes inference framework. Experiments are performed on some changeling public and our own video sequences. The experimental results demonstrate that the proposed method achieves considerable performance.