  Human action recognition still exists many challenging problems such as
different viewpoints, occlusion, lighting conditions, human body size and the
speed of action execution, although it has been widely used in different areas.
To tackle these challenges, the Kinect depth sensor has been developed to
record real time depth sequences, which are insensitive to the color of human
clothes and illumination conditions. Many methods on recognizing human action
have been reported in the literature such as HON4D, HOPC, RBD and HDG, which
use the 4D surface normals, pointclouds, skeleton-based model and depth
gradients respectively to capture discriminative information from depth videos
or skeleton data. In this research project, the performance of four
aforementioned algorithms will be analyzed and evaluated using five benchmark
datasets, which cover challenging issues such as noise, change of viewpoints,
background clutters and occlusions. We also implemented and improved the HDG
algorithm, and applied it in cross-view action recognition using the UWA3D
Multiview Activity dataset. Moreover, we used different combinations of
individual feature vectors in HDG for performance evaluation. The experimental
results show that our improvement of HDG outperforms other three
state-of-the-art algorithms for cross-view action recognition.
