Torpey, David2020-02-072020-02-072019https://hdl.handle.net/10539/28820A 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 2019This 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 datasetsenThesis