Human action recognition using local two-stream CNN features with SVMs

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dc.contributor.author Torpey, David
dc.date.accessioned 2020-02-07T07:19:46Z
dc.date.available 2020-02-07T07:19:46Z
dc.date.issued 2019
dc.identifier.uri https://hdl.handle.net/10539/28820
dc.description A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfillment of the requirements for the degree of Master of Science. May 2019 en_ZA
dc.description.abstract This dissertation serves to survey existing methods for human action recognition; compare those existing methods on some of the publicly-available benchmark datasets; and to introduce a novel method to solve the problem of human action recognition. The proposed method separately extracts appearance and motion features using state-of-the-art three-dimensional convolutional neural networks from sampled snippets of a video. These local features are then concatenated to form global representations for the videos. These global feature vectors are then used to train a linear SVM to perform the action classification. Additionally, we show the benefit of performing two simple, intuitive pre-processing steps, termed crop filling and optical flow scaling. We test the method extensively, and report results on the KTH and HMDB51 datasets en_ZA
dc.language.iso en en_ZA
dc.title Human action recognition using local two-stream CNN features with SVMs en_ZA
dc.type Thesis en_ZA
dc.description.librarian M T 2019 en_ZA


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