Action Recognition Utilizing YGAR Dataset
Shuo Wang Amiya Ranjan Lawrence Jiang
The scarcity of high quality actions video data is a bottleneck in the
research and application of action recognition. Although significant effort has
been made in this area, there still exist gaps in the range of available data
types a more flexible and comprehensive data set could help bridge. In this
paper, we present a new 3D actions data simulation engine and generate 3 sets
of sample data to demonstrate its current functionalities. With the new data
generation process, we demonstrate its applications to image classifications,
action recognitions and potential to evolve into a system that would allow the
exploration of much more complex action recognition tasks. In order to show off
these capabilities, we also train and test a list of commonly used models for
image recognition to demonstrate the potential applications and capabilities of
the data sets and their generation process.