Efficient Key Frame Extraction from Videos Using Convolutional Neural Networks and Clustering Techniques

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One of the most reliable information sources is video, and in recent years, online and offline video consumption has increased to an unprecedented degree. One of the main difficulties in extracting information from videos is that unlike images, where information can be gleaned from a single frame, a viewer must watch the entire video in order to comprehend the context. In this work, we try to use various algorithmic techniques, such as deep neural networks and local features, in conjunction with a variety of clustering techniques, to find an efficient method of extracting interesting key frames from videos to summarize them. Video summarization plays a major role in video indexing, browsing, compression, analysis, and many other domains. One of the fundamental elements of video structure analysis is key frame extraction, which pulls significant frames out of the movie. An important frame from a video that may be used to summarize videos is called a key frame. We provide a technique that leverages convolutional neural networks in our suggested model, static video summarization, and key frame extraction from movies.