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Practical Matters in Computer Vision Read chapter Read first chapter

2015 | OriginalPaper | Chapter

2. Human Action Recognition: Contour-Based and Silhouette-Based Approaches

Authors : Salim Al-Ali, Mariofanna Milanova, Hussain Al-Rizzo, Victoria Lynn Fox

Published in: Computer Vision in Control Systems-2

Publisher: Springer International Publishing

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Abstract

Human action recognition in videos is a desired field in computer vision applications since it can be applied in human computer interaction, surveillance monitors, robot vision, etc. Two approaches of features are investigated in this chapter. First approach is a contour-based type. Four features are investigated in this approach such as Cartesian Coordinate Features (CCF), Fourier Descriptors Features (FDF), Centroid-Distance Features (CDF), and Chord-Length Features (CLF). The second approach is a silhouette-based type. Three features are investigated in this approach such as Histogram of Oriented Gradients (HOG), Histogram of Oriented Optical Flow (HOOF), and Structural Similarity Index Measure (SSIM) features. All these features are simple to compute, efficient to classify, and fast to calculate. Therefore, these features demonstrate a promising field for human action recognition. Moreover, the classification is achieved using two classifiers: K-Nearest-Neighbor (KNN) and Support Vector Machine (SVM). The experimental results demonstrated that these features have a promising potential and useful for the human action recognition in videos.

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Metadata
Title
Human Action Recognition: Contour-Based and Silhouette-Based Approaches
Authors
Salim Al-Ali
Mariofanna Milanova
Hussain Al-Rizzo
Victoria Lynn Fox
Copyright Year
2015
Book
Computer Vision in Control Systems-2

Print ISBN: 978-3-319-11429-3

Electronic ISBN: 978-3-319-11430-9

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-11430-9_2

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