Summarization Extraction Method for Surveillance Video Based on Color Spatial Distribution Characteristic

Chang, Jun; Hu, Ruimin; Hua, Zhang

doi:10.1109/cise.2009.5363959

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…For example, in content-based, features such as motion, sound, color, etc. are used for key frame extraction, while the main idea of cluster-based approaches is to select the frame closest to each cluster centroid as a key frame [4].…”

Section: Introductionmentioning

confidence: 99%

New Approach in Video Summarization Based on Color Feature

Sarmadi¹

2017

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Video summarization is a process to create a meaningful summary of the recorded video within a short period of time. In recent years due to increase in generating videos, we need to summarize videos. General video summarization methods are divided into static and dynamic. Static summary is done by using key frames. However, the dynamic video summarization includes a sequence of small video shots that have been put together in chronological order. In this paper, video summarization is done by using static method and color-based key frame extraction. Then by applying the super resolution algorithm on selected key frames, we obtained frames with higher visual quality. The summarization was performed on several videos. Simulation results show our method produces summaries with high visual quality and computational speed.

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Section: Introductionmentioning

confidence: 99%

New Approach in Video Summarization Based on Color Feature

Sarmadi¹

2017

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“…The most of the methods uses motion information to detect abrupt changes (Li et al, 2011;Shao & Ji, 2009). The spatial color distribution can also be used to detect abrupt change (Chang et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

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

Raikwar

Bhatnagar

Jalal

2015

International Journal of System Dynamics Applications

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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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