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2018 | OriginalPaper | Chapter

Part-Activated Deep Reinforcement Learning for Action Prediction

Authors : Lei Chen, Jiwen Lu, Zhanjie Song, Jie Zhou

Published in: Computer Vision – ECCV 2018

Publisher: Springer International Publishing

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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.

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Title: Part-Activated Deep Reinforcement Learning for Action Prediction
Authors: Lei Chen
Jiwen Lu
Zhanjie Song
Jie Zhou
Publisher: Springer International Publishing
Book: Computer Vision – ECCV 2018
Print ISBN: 978-3-030-01218-2

Electronic ISBN: 978-3-030-01219-9

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-030-01219-9_26

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