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

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: KNearest-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. S. Al-Ali (&) M. Milanova Department of Computer Science, University of Arkansas at Little Rock, 2801 S. University Avenue, Little Rock, AR 72204, USA e-mail: [email protected] M. Milanova e-mail: [email protected] H. Al-Rizzo Department of System Engineering, University of Arkansas at Little Rock, 2801 S. University Avenue, Little Rock, AR 72204, USA e-mail: [email protected] V.L. Fox Department of Applied Science, University of Arkansas at Little Rock, 2801 S. University Avenue, Little Rock, AR 72204, USA e-mail: [email protected] © Springer International Publishing Switzerland 2015 M.N. Favorskaya and L.C. Jain (eds.), Computer Vision in Control Systems-2, Intelligent Systems Reference Library 75, DOI 10.1007/978-3-319-11430-9_2 11