  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.

Computer Vision and Pattern Recognition;Machine Learning;Machine Learning