Joint Angles Similarities and HOG2 for Action Recognition

We propose a set of features derived from skeleton tracking of the human body and depth maps for the purpose of action recognition. The descriptors proposed are easy to implement, produce relatively small-sized feature sets, and the multi-class classification scheme is fast and suitable for real-tim...

Full description

Saved in:
Bibliographic Details
Published in2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops pp. 465 - 470
Main Authors Ohn-Bar, Eshed, Trivedi, Mohan M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2013
Subjects
Accuracy
Feature extraction
Histograms
Joints
Trajectory
Vectors
Online AccessGet full text

Cover

More Information
Summary:We propose a set of features derived from skeleton tracking of the human body and depth maps for the purpose of action recognition. The descriptors proposed are easy to implement, produce relatively small-sized feature sets, and the multi-class classification scheme is fast and suitable for real-time applications. We intuitively characterize actions using pairwise affinities between view-invariant joint angles features over the performance of an action. Additionally, a new descriptor for spatio-temporal feature extraction from color and depth images is introduced. This descriptor involves an application of a modified histogram of oriented gradients (HOG) algorithm. The application produces a feature set at every frame, and these features are collected into a 2D array which then the same algorithm is applied to again (the approach is termed HOG 2 ). Both feature sets are evaluated in a bag-of-words scheme using a linear SVM, showing state-of-the-art results on public datasets from different domains of human-computer interaction.
ISSN:2160-7508
2160-7516
DOI:10.1109/CVPRW.2013.76