Key-frame selection for automatic summarization of surveillance videos: a method of multiple change-point detection

Recent years have witnessed a drastic growth of various videos in real-life scenarios, and thus there is an increasing demand for a quick view of such videos in a constrained amount of time. In this paper, we focus on automatic summarization of surveillance videos and present a new key-frame selecti...

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Bibliographic Details
Published in:Machine vision and applications 2018-10, Vol.29 (7), p.1101-1117
Main Authors: Gao, Zhen, Lu, Guoliang, Lyu, Chen, Yan, Peng
Format: Article
Language:English
Subjects:
Change detection
Clips
Clustering
Communications Engineering
Computer Science
Divergence
Image Processing and Computer Vision
Networks
Original Paper
Pattern Recognition
Surveillance
Vision systems
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Summary:Recent years have witnessed a drastic growth of various videos in real-life scenarios, and thus there is an increasing demand for a quick view of such videos in a constrained amount of time. In this paper, we focus on automatic summarization of surveillance videos and present a new key-frame selection method for this task. We first introduce a dissimilarity measure based on f -divergence by a symmetric strategy for multiple change-point detection and then use it to segment a given video sequence into a set of non-overlapping clips. Key frames are extracted from the resulting video clips by a typical clustering procedure for final video summary. Through experiments on a wide range of testing data, excellent performances, outperforming given state-of-the-art competitors, have been demonstrated which suggests good potentials of the proposed method in real-world applications.
ISSN:0932-8092
1432-1769
DOI:10.1007/s00138-018-0954-7