Object Tracking using Incremental 2D-PCA Learning and ML Estimation
Tiesheng Wang, Irene Yu‐Hua Gu, Pengfei Shi · 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 blob-tracking 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 single moving object in dynamic backgrounds, which have shown good tracking results. Comparisons with the method using the conventional PCA were also made.