The research work in each category is investigated, compared and analyzed on the basis of various intrinsic characteristics. 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 survey also presents genre-wise datasets description, various evaluation techniques and future recommendations. The presented work provides general pipeline and broad classification of video summarization systems. ![]() 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. ![]() In the past two decades, several summarization techniques ranging from conventional non-learning to deep learning based mechanisms have been developed. ![]() 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. Video summarization deals with the generation of a condensed version of the original video by including meaningful frames or segments while eliminating redundant information.
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