Object Tracking using Incremental 2D-PCA Learning and ML Estimation
Object Tracking using Incremental 2D-PCA Learning and ML Estimation
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DOI：
10.1109/icassp.2007.366062
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发表时间：
2007-04
期刊：
影响因子：
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通讯作者：
Tiesheng Wang;I. Gu;P. Shi
中科院分区：
文献类型：
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作者：
Tiesheng Wang;I. Gu;P. Shi
近年来，视频监控引起了越来越多的兴趣。本文解决了视频中运动物体的跟踪问题。提出了一种两步处理方法：基于增量2DPCA（二维主成分分析）的方法用于给定跟踪区域的对象特征，以及基于对象特征和先前blob序列的ML（最大似然）blob跟踪过程。提出的增量2DPCA递归更新行投影和列投影协方差矩阵，对于动态对象的在线学习具有更高的计算效率。所提出的机器学习斑点跟踪同时考虑了形状信息和目标特征。对动态背景下包含一系列单一运动目标的室内和室外图像序列进行了测试和评估，取得了良好的跟踪效果。并与传统主成分分析方法进行了比较。
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.