一种鲁棒的摄像机运动分类算法

摄像机运动分类是基于内容的视频分析和理解的重要问题.本文通过对运动矢量场的分析,提出了一种基于统计学习的、分层次的摄像机运动分类算法.该算法利用支持向量机(SVM)在有限样本条件下的学习能力,实现摄像机运动类型的初步分类;然后,充分考虑运动矢量场的方向和位置信息,进一步区分缩放和旋转操作,并识别摄像机平移操作的方向.算法在运动矢量的预处理过程中引入摄影规则,有效地降低了前景运动噪声的影响.

A Robust Approach to Camera Motion Classification

Camera motion classification is an important issue in content-based video analysis.In this paper,a robust and hierarchical camera motion classification approach based on statistical learning is proposed.As Support Vector Machines(SVM) has a very good learning capacity with limited sample set without incorporating problem domain knowledge,in the first step,SVM is employed to classify camera motion operations into two classes:translation and non-translation operations.Then,rotation and zoom operations are distinguished using motion vectors' location and direction.The direction of translation operation is also identified.In the pre-processing step,cinematic rule is utilized to filter atypical noise and foreground motion noise.