Joint Selection using Deep Reinforcement Learning for Skeleton-based
Skeleton based human activity recognition has attracted lots of
attention due to its wide range of applications. Skeleton data includes
two or three dimensional coordinates of body joints. All of the body
joints are not effective in recognizing different activities, so finding
key joints within a video and across different activities has a
significant role in improving the performance. In this paper we propose
a novel framework that performs joint selection in skeleton video frames
for the purpose of human activity recognition. To this end, we formulate
the joint selection problem as a Markov Decision Process (MDP) where we
employ deep reinforcement learning to find the most informative joints
per frame. The proposed joint selection method is a general framework
that can be employed to improve human activity classification methods.
Experimental results on two benchmark activity recognition data sets
using three different classifiers demonstrate effectiveness of the
proposed joint selection method.