Year: 2020
Type: preprint
Abstract: Video summarisation can be posed as the task of extracting important parts of a video in order to create an informative summary of what occurred in the video. In this paper we introduce SummaryNet as a supervised learning framework for automated video summarisation. SummaryNet employs a two-stream convolutional network to learn spatial (appearance) and temporal (motion) representations. It utilizes an encoder-decoder model to extract the most salient features from the learned video representations. Lastly, it uses a sigmoid regression network with bidirectional long short-term memory cells to predict the probability of a frame being a summary frame. Experimental results on benchmark datasets show that the proposed method achieves comparable or significantly better results than the state-of-the-art video summarisation methods.
Source: arXiv (Cornell University)
Authors: Jappie, Ziyad, Torpey, David, Celik, Turgay
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3007136396