A Study on various Key Frame Detection Techniquesin order to Develop an Efficient Method

Globally, enormous amounts of text, photos, and blog content are produced. Due to improvements in network designs, high storage availability, and widespread use of digital cameras, video processing has become increasingly important. High-definition digital video has rapidly replaced dull text material, nevertheless, as a result of the extraordinary advancements in multimedia and Internet technology. This has made it one of the primary means through which people disseminate information. Each video consists of a large number of frames that are crucial pieces of information. To process the video, it is essential to extract these frames. Identifying key frames from every frame that contain distinctive aspects of the video aids in the development of cutting-edge technologies that support a variety of video analytics applications, such as object and anomaly detection. In this study, a comparison of traditional key-frame extraction methods, viz., Clustering, Motion Analysis, and Shot-Boundary based, as well as deep learning key-frame extraction methods is done. In this study, existing deep learning key-frame extraction approaches are compared to more established key-frame extraction techniques like clustering, motion analysis, and shot-boundary based methods. The aforementioned techniques have been implemented, and a thorough comparison study has been provided along with a list of their benefits and drawbacks.