Video Summarization using Keyframe Extraction Methods
Ajay Mushan1, P. S. Vidap2
1Ajay Mushan, Student, Department of Computer Engineering, Pune Institute of Computer Technology, Pune, India.
2Prof. Pujashree Vidap, Associate Professor, Department of Computer Engineering, Pune Institute of Computer Technology, Pune, India.
Manuscript received on May 25, 2020. | Revised Manuscript received on June 29, 2020. | Manuscript published on July 30, 2020. | PP: 1030-1032 | Volume-9 Issue-2, July 2020. | Retrieval Number: B4043079220/2020©BEIESP | DOI: 10.35940/ijrte.B4043.079220
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Video summarization plays an important role in too many fields, such as video indexing, video browsing, video compression, video analyzing and so on. One of the fundamental units in the video structure analysis is the keyframe extraction, Keyframe provides meaningful frames from the video. The keyframe consists of the meaningful frame from the videos which help for video summarization. In this proposed model, we presented an approach that is based on Convolutional Neural Network, keyframe extraction from videos and static video summarization. First, the video should be converted to frames. Then we perform redundancy elimination techniques to reduce the redundancy from frames. Then extract the keyframes from video by using the Convolutional Neural Network(CNN) model. From the extracted keyframe, we form a video summarization.
Keywords: Video Summarization, Keyframes, Convolutional Neural Network, key frame extraction, interest point.