Automated Video Clipping using Deep Learning

Abstract

This is due to delving into the realm of video summarization (VS) employing deep learning techniques, offering insights into methodologies, challenges, applications, and recent developments within this domain. A broad survey is led on different profound learning approaches, calculations, and methodologies for video synopsis, which has a particular spotlight on the promising use of Generative Adversarial  Networks (GANs) for unaided video summarization. The invention included evaluation protocols, and datasets, that highlight the efficacy of the Multi-function optimized Long Short Term Memory of Recurrent Neural Networks for Video Summarization. Model selection in VS techniques is addressed, underlining the necessity for community appreciation and clarity in deciding processes. Advanced techniques are classed into quality-based video summarization, group-based video summarization, photo selection, occurrence-based, and path-based video summarization. The spectrum of action-based learning methods is dissected; distinguishing between activities, weakly supervised, individual, and support learning approaches. Applications span various domains, including sports, doubt-focused screenplays, surveillance, films, newsreels, medical uses, and education systems.