NDLI: Scalable temporal interest points for abstraction and classification of video events

Content Provider	IEEE Xplore Digital Library
Author	Seung-Hoon Han ln-So Kweon
Copyright Year	2005
Description	Author affiliation: Digital Media R&D Center, Samsung Electron. Co., South Korea (Seung-Hoon Han)
Abstract	The image sequence of a static scene includes similar or redundant information over time. Hence, motion-discontinuous instants can efficiently characterize a video shot or event. However, such instants (key frames) are differently identified according to the change of velocity and acceleration of motion, and such scales of change might be different on each sequence of the same event. In this paper, we present a scalable video abstraction in which the key frames are obtained by the maximum curvature of camera motion at each temporal scale. The scalability means dealing with the velocity and acceleration change of motion. In the temporal neighborhood determined by the scale, the scene features (motion, color, and edge) can be used to index and classify the video events. Therefore, those key frames provide temporal interest points (TIPs) for the abstraction and classification of video events.
File Size	293319
File Format	PDF
ISBN	0780393317
DOI	10.1109/ICME.2005.1521512
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-07-06
Publisher Place	Netherlands
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Layout Acceleration Cameras Humans Research and development Robot vision systems Computer vision Laboratories Image sequences Scalability
Content Type	Text
Resource Type	Article

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