A robust and hierarchical approach for camera motion classification

Authors:
Yuliang Geng;De Xu;Songhe Feng;Jiazheng Yuan
Affiliations:
Institute of Computer Science and Technology, Beijing Jiaotong University, Beijing, China;Institute of Computer Science and Technology, Beijing Jiaotong University, Beijing, China;Institute of Computer Science and Technology, Beijing Jiaotong University, Beijing, China;Institute of Computer Science and Technology, Beijing Jiaotong University, Beijing, China
Venue:
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Year:
2006

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Abstract

Camera motion classification is an important issue in content-based video retrieval. In this paper, a robust and hierarchical camera motion classification approach is proposed. As the Support Vector Machine (SVM) has a very good learning capacity with limited sample set and does not require any heuristic parameter, the SVM is first employed to classify camera motions into translation and non-translation motions in preliminary classification. In this step, four features are extracted as input of the SVM. Then, zoom and rotation motions are further classified by analyzing the motion vectors’ distribution. And the directions of translation motions are also identified. The experimental results show that the proposed approach achieves a good performance.