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Demystifying Human Action Recognition in Deep Learning with Space-Time Feature Descriptors


Research Paper (postgraduate), 2018

33 Pages


Excerpt


Contents

Abstract..

1 Introduction..

2 Background and Related Work..
2.1 Feature Extraction and Descriptor Representation..
2.1.1 Space-Time Interest Points (STIP)..
2.1.2 Dense Sampling..
2.1.3 Histogram of Oriented Gradients (HOG)..
2.1.4 N-Jets..
2.1.5 Histograms of Oriented Optical Flow (HOF)..
2.1.6 Feature Combination..
2.2 Learning Algorithms..
2.2.1 Support Vector Machines (SVM)..
2.2.2 Convolutional Neural Networks (CNN)..
2.2.3 Recurrent Neural Networks (RNNs)..
2.3 Conclusion..

3 Research Method..
3.1 Research Hypothesis..
3.2 Methodology..
3.2.1 Phase 1: Implementation..
3.2.2 Phase 2: Training..
3.2.3 Phase 3: Testing..
3.3 Motivation for Method..
3.3.1 Features..
3.3.2 Classier..
3.4 Conclusion..

4 Research Plan 21 4.1 Deliverables.
4.1.1 Phase 1: Implementation..
4.1.2 Phase 2: Training..
4.1.3 Phase 3: Testing..
4.2 Potential Issues..
4.2.1 Lengthy Training Time..
4.2.2 Low Accuracies..
4.3 Conclusion..

5 Conclusion.

Excerpt out of 33 pages

Details

Title
Demystifying Human Action Recognition in Deep Learning with Space-Time Feature Descriptors
Course
Machine Learning
Author
Year
2018
Pages
33
Catalog Number
V413235
ISBN (eBook)
9783668642591
ISBN (Book)
9783668642607
File size
1419 KB
Language
English
Keywords
Wits
Quote paper
Mike Nkongolo (Author), 2018, Demystifying Human Action Recognition in Deep Learning with Space-Time Feature Descriptors, Munich, GRIN Verlag, https://www.grin.com/document/413235

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