Cart (Loading....) | Create Account
Close category search window

Summarizing Rushes Videos by Motion, Object, and Event Understanding

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Feng Wang ; Dept. of Comput. Sci. & Technol., East China Normal Univ., Shanghai, China ; Chong-Wah Ngo

Rushes footages are considered as cheap gold mine with the potential for reuse in broadcasting and filmmaking industries. However, mining “gold” from unedited videos such as rushes is challenging as the reusable segments are buried in a large set of redundant information. In this paper, we propose a unified framework for stock footage classification and summarization to support video editors in navigating and organizing rushes videos. Our approach is composed of two steps. First, we employ motion features to filter the undesired camera motion and locate the stock footage. A hierarchical hidden Markov model (HHMM) is proposed to model the motion feature distribution and classify video segments into different categories to decide their potential for reuse. Second, we generate a short video summary to facilitate quick browsing of the stock footages by including the objects and events that are important for storytelling. For objects, we detect the presence of persons and moving objects. For events, we extract a set of features to detect and describe visual (motion activities and scene changes) and audio events (speech clips). A representability measure is then proposed to select the most representative video clips for video summarization. Our experiments show that the proposed HHMM significantly outperforms other methods based on SVM, FSM, and HMM. The automatically generated rushes summaries are also demonstrated to be easy-to-understand, containing little redundancy, and capable of including ground-truth objects and events with shorter durations and relatively pleasant rhythm based on the TRECVID 2007, 2008, and our subjective evaluations.

Published in:

Multimedia, IEEE Transactions on  (Volume:14 ,  Issue: 1 )

Date of Publication:

Feb. 2012

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.