Recurrent generative adversarial networks for unsupervised WCE video summarization

Video summarization using deep learning techniques: a detailed analysis and investigation

One of the critical multimedia analysis problems in today’s digital world is video summarization (VS). Many VS methods have been suggested based on deep learning methods. Nevertheless, These are inefficient in processing, extracting, and deriving information in the minimum amount of time from long-duration videos. Detailed analysis and investigation of numerous deep learning approach accomplished to determine root of problems connected with different deep learning methods in identifying and summarizing the essential activities in such videos. Various deep learning techniques have been investigated and examined to detect the event and summarization capability for detecting and summarizing multiple activities. Keyframe selection Event detection, categorization, and the activity feature summarization correspond to each activity. The limitations related to each category are also discussed in depth. Concerns about detecting low activity using the deep network on various types of public datasets are also discussed. Viable strategies are suggested to evaluate and improve the generated video summaries on such datasets. Moreover, Potential recommended applications based on literature are listed out. Various deep learning tools for experimental analysis have also been discussed in the paper. Future directions are presented for further exploration of research in VS using deep learning strategies.