Recent Challenges and Opportunities in Video Summarization With Machine Learning Algorithms

Abstract

The fast progress in digital technology has sparked the generation of the amount of voluminous data from different social media platforms like Instagram, Facebook, YouTube, etc. There are other platforms, as well which generate large data like News, CCTV videos, sports, entertainment, etc. Lengthy Videos typically contain a significant number of duplicate occurrences that are uninteresting to the viewer. Eliminating this unnecessary information and concentra only on the crucial events will be far more advantageous. This produces a summary of lengthy films, which can save viewers time and enable better memory management. The highlights of a lengthy video are condensed into a video summary. Video summarization is an essential topic today, since many industries have CCTV cameras installed for various reasons such as monitoring, security, and tracking. Because surveillance videos are taken 24 hours a day, enormous amounts of memory and time are required if one wishes to trace any incident or person from the full day's video. The summary generated from multiple views is far more challenging, so more study and advancement in MVS is required. The conceptual basis of video summarizing approaches is thoroughly addressed in this paper. This paper addresses applications and technology challenges in Single view and Multi View summarization.