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A Novel Framework for Efficient Extraction of Meaningful Key Frames from Surveillance Video

A Novel Framework for Efficient Extraction of Meaningful Key Frames from Surveillance Video

Suresh Chandra Raikwar, Charul Bhatnagar, Anand Singh Jalal
Copyright: © 2015 |Volume: 4 |Issue: 2 |Pages: 18
ISSN: 2160-9772|EISSN: 2160-9799|EISBN13: 9781466680364|DOI: 10.4018/ijsda.2015040104
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MLA

Raikwar, Suresh Chandra, et al. "A Novel Framework for Efficient Extraction of Meaningful Key Frames from Surveillance Video." IJSDA vol.4, no.2 2015: pp.56-73. https://doi.org/10.4018/ijsda.2015040104

APA

Raikwar, S. C., Bhatnagar, C., & Jalal, A. S. (2015). A Novel Framework for Efficient Extraction of Meaningful Key Frames from Surveillance Video. International Journal of System Dynamics Applications (IJSDA), 4(2), 56-73. https://doi.org/10.4018/ijsda.2015040104

Chicago

Raikwar, Suresh Chandra, Charul Bhatnagar, and Anand Singh Jalal. "A Novel Framework for Efficient Extraction of Meaningful Key Frames from Surveillance Video," International Journal of System Dynamics Applications (IJSDA) 4, no.2: 56-73. https://doi.org/10.4018/ijsda.2015040104

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Abstract

The key frame extraction, aimed at reducing the amount of information from a surveillance video for analysis by human. The key frame is an important frame of a video to provide an overview of the video. Extraction of key frames from surveillance video is of great interest in effective monitoring and later analysis of video. The computational cost of the existing methods of key frame extraction is very high. The proposed method is a framework for Key frame extraction from a long surveillance video with significantly reduced computational cost. The proposed framework incorporates human intelligence in the process of key frame extraction. The results of proposed framework are compared with the results of IMARS (IBM multimedia analysis and retrieval system), results of the key frame extraction methods based on entropy difference method, spatial color distribution method and edge histogram descriptor method. The proposed framework has been objectively evaluated by fidelity. The experimental results demonstrate evidence of the effectiveness of the proposed approach.

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