Human action recognition using local two-stream CNN features with SVMs
Date
2019
Authors
Torpey, David
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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
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