Digital Video Summarization Techniques: A Survey
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IJERTV9IS010026
Digital Video Summarization Techniques: A Survey
Ashenafi Workie , Rajesh Sharma , Yun Koo Chung
Video summarization which gives a short and precise representation of original video clips by showing the most representative synopsis is gaining more attention. The main objective of Video summarization is to provide a clear analysis of the video by removing redundant and extracting key frames contents from the video. The architecture in video summarization shows how a large video skims in to short and story contents. Many types of research were done in the past and ongoing until now. Therefore, multiple methods and techniques proposed by researchers from classical computer vision until the recent deep learning approaches. Most literature shows that most of the video generation and summarization approaches shift into deep generative models and variational auto encoders. These techniques may fall into summarized, unsupervised and deep reinforcement learning approaches. Video representation categorized in static and dynamic summarization ways. But video summarization still challenging with different problems, these are computational devices, complexity, and lack of dataset are some them. The effective implementation of video summarization applied in different real-world scenarios like movies tailor in the film industry, highlight in football soccer, anomaly detection video surveillance system.
