Compact Representations of Videos Through Dominant and Multiple Motion Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
On clustering and retrieval of video shots
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Video Mosaics for Virtual Environments
IEEE Computer Graphics and Applications
Rapid estimation of camera motion from compressed video with application to video annotation
IEEE Transactions on Circuits and Systems for Video Technology
Hierarchical video summarization based on video structure and highlight
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
A robust and hierarchical approach for camera motion classification
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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Due to the fact that the camera motion usually imply some hints which are helpful in bridging the gap between computationally available features and semantic interpretations, extensive researches have been executed to extract them for various purposes. However, these strategies fail to classify the camera rotation; furthermore, their performance might be significantly reduced by considerable noise or error in extracted features. In this paper, a robust camera motion classification strategy is proposed. We use the mutual relationship between motion vectors for motion classification. Given any two motion vectors in each P-frame, four types of mutual relationships between them are classified, then, a 14-bins feature vector is constructed to characterize the statistical motion information for the P-frame. Finally, the qualitative classification is executed by considering all achieved statistical information.