Abstract. In this paper, we propose a part-activated deep reinforcement learning (PA-DRL) method for action prediction. Most existing
methods for action prediction utilize the evolution of whole frames to
model actions, which cannot avoid the noise of the current action, especially in the early prediction. Moreover, the loss of structural information
of human body diminishes the capacity of features to describe actions.
To address this, we design the PA-DRL to exploit the structure of the
human body by extracting skeleton proposals under a deep reinforcement
learning framework. Specifically, we extract features from different parts
of the human body individually and activate the action-related parts in
features to enhance the representation. Our method not only exploits the
structure information of the human body, but also considers the saliency
part for expressing actions. We evaluate our method on three popular action prediction datasets: UT-Interaction, BIT-Interaction and UCF101.
Our experimental results demonstrate that our method achieves the performance with state-of-the-arts.