SummaryNet: two-stream convolutional networks for automatic video summarisation

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dc.contributor.author Jappie, Ziyad
dc.date.accessioned 2020-11-16T22:45:16Z
dc.date.available 2020-11-16T22:45:16Z
dc.date.issued 2020
dc.identifier.uri https://hdl.handle.net/10539/30207
dc.description A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Science, 2020 en_ZA
dc.description.abstract Video summarisation is the task of automatically summarising a video sequence, to extract “important” parts of the video so as to give an overview of what has occurred. The benefit of solving this problem is that it can be applied to a myriad of fields such as the entertainment industry, sports, e-learning and many more. There is a distinct inherent difficulty with video summarisation due to its subjectivity - there is no one defined correct answer. As such, it is particularly difficult to define and measure tangible performance. This is in addition to the other difficulties associated with general video processing. We present a novel two-stream network framework for automatic video summarisation, which we call SummaryNet. The SummaryNet employs a deep two-stream network to model pertinent spatio-temporal features by leveraging RGB as well as optical flow information. We use the Two-Stream Inflated 3D ConvNet (I3D) network to extract high-level, semantic feature representations as inputs to our SummaryNet model. Experimental results on common benchmark datasets show that the considered method achieves comparable or better results than the state-of-the-art video summarisation methods en_ZA
dc.language.iso en en_ZA
dc.title SummaryNet: two-stream convolutional networks for automatic video summarisation en_ZA
dc.type Thesis en_ZA
dc.description.librarian CK2020 en_ZA
dc.faculty Faculty of Science en_ZA
dc.school School of Computer Science and Applied Mathematics en_ZA


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