Key Frame Extraction Algorithm for Surveillance Video Based on Golden Section

Abstract

The traditional key frame extraction algorithm can't effectively segment the surveillance video, and it can't focus on the moving objects in the surveillance video data. In this paper, a key frame extraction algorithm based on golden section is proposed. The method first detects and tracks the moving target in the surveillance video, and calculates the entropy value of the foreground image. Secondly, the golden section in mathematical calculation is introduced to divide the sub-segment of the surveillance video, and the standard deviation of the foreground image entropy is used to measure the intra-frame similarity of the video sub-segments. If the intra-frame similarity is low, the frame at the golden section point is selected as the video key frame;if the difference within the video segment is large, golden section is continued until there is no significant difference in the video frames in all video sub-segments. in all the video sub-segments. Through experimental tests on a variety of surveillance video data and comparison with traditional algorithms, the experimental results show that the proposed algorithm effectively compresses the original surveillance video, and can extract the moving objects in the surveillance video more completely.