Object Tracking using Incremental 2D-PCA Learning and ML Eestimation

Paper i proceeding, 2007

Video surveillance has drawn increasing interests in recent years. This paper addresses the issue of moving object tracking from videos. A two-step processing procedure is proposed: an incremental 2DPCA (two-dimensional Principal Component Analysis)-based method for characterizing objects given the tracked regions, and a ML (Maximum Likelihood) blob-tracking process given the object characterization and the previous blob sequence. The proposed incremental 2DPCA updates the row- and column-projected covariance matrices recursively, and is computationally more efficient for online learning of dynamic objects. The proposed ML blobtracking takes into account both the shape information and object characteristics. Tests and evaluations were performed on indoor and outdoor image sequences containing a range of moving objects in dynamic backgrounds, which have shown good tracking results. Comparisons with the method using the conventional PCA were also made.