A comprehensive study of automatic video summarization techniques

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Video summarization deals with the generation of a condensed version of the original video by including meaningful frames or segments while eliminating redundant information. The main challenge in a video summarization task is to identify important frames or segments corresponding to human perception which varies from one genre to another. In the past two decades, several summarization techniques ranging from conventional non-learning to deep learning based mechanisms have been developed. This study provides a comprehensive survey focusing on the massive literature with scope ranging from general to domain specific methods, single view to multi-view processes, generic to user-interaction based mechanisms and conventional to deep learning-based approaches. The presented work provides general pipeline and broad classification of video summarization systems. The survey also presents genre-wise datasets description, various evaluation techniques and future recommendations. The key-points of presented work lie in its approach of analyzing literature in a systematic manner and its wide coverage by including some of the domains that have been overlooked over the time like aerial videos, medical videos and user-customization based approaches. The research work in each category is investigated, compared and analyzed on the basis of various intrinsic characteristics. The main objective of this manuscript is to guide future researchers about state-of-the-art work done in various domains of the video summarization field, so that the scope and performance of automatic video summarization systems can be enhanced further by designing new approaches or by improving different existing techniques.


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