Geometric Model for Human Body Orientation Classification
Abstract: This paper proposes an approach
for cal- culating and
estimating human body orientation using geometric model. A novel framework
integrating gradient shape and texture model of the human body orientation is
proposed. The gradient is a natural way for describing the human shapes, while the texture explains the body characteristic. The
framework is then combined with the
random forest classifier to obtain a
robust class differ- ence
of the human body orientation. Experiments and comparison results are
provided to show the advantages of our system over state-of-the-art. For both
modeled and un-modeled gradient-texture
features with random forest classifier, they achieve the highest
accuracy on separating each human orientation
class, respectively 56.9% and
67.3% for TUD-Stadtmitte dataset.
Keywords: Human Body
Orientation; Histogram of Oriented Gradient; Local Binary Pattern; Geometric
Model
Author: Igi Ardiyanto
Journal Code: jptinformatikagg150016
