Efficient Key Frame Extraction based on Adaptive Threshold and HOG for Video Summarization

Key frame (KF) extraction process is considered as a crucial task in video structure analysis, it plays important role in video summarization, content analysis, video compression and so on. This process aimed to give a good video summarization by extracting the frame or set of frames that provide a comprehensive representation for the video sequence and removing the other frames that are considered redundant. In this paper, a new method has been proposed, the proposed method utilized Histogram of Oriented Gradient (HOG) as feature and adaptive thresholding technique, enabling the detection of substantial changes in visual content between consecutive frames in each video shot. By calculating the HOG differences and applying adaptive threshold, the method divides the shot's frames into groups, then from each groups a key frame with substantial features is selected, while other frames are considered as redundant and removed. Experimental evaluations on different videos, shows that the proposed methods able to extract key frames that accurately reflect the video's primary content and produce a good summarization. The results reduce the redundancy while preserving the essential idea of the video.