DeepActsNet: Spatial and Motion features from Face, Hands, and Body Combined with Convolutional and Graph Networks for Improved Action Recognition

TL;DR

DeepActsNet is a novel deep learning model that combines spatial and motion features from face, hands, and body skeleton data using convolutional and graph networks to improve action recognition accuracy.

Contribution

The paper introduces Deep Action Stamps and DeepActsNet, integrating face, hand, and body data with convolutional and graph networks for enhanced action recognition.

Findings

Significant accuracy improvements on NTU60, NTU120, and SYSU datasets.

Reduced computational cost compared to state-of-the-art methods.

Effective fusion of face, hand, and body features for action recognition.

Abstract

Existing action recognition methods mainly focus on joint and bone information in human body skeleton data due to its robustness to complex backgrounds and dynamic characteristics of the environments. In this paper, we combine body skeleton data with spatial and motion features from face and two hands, and present "Deep Action Stamps (DeepActs)", a novel data representation to encode actions from video sequences. We also present "DeepActsNet", a deep learning based ensemble model which learns convolutional and structural features from Deep Action Stamps for highly accurate action recognition. Experiments on three challenging action recognition datasets (NTU60, NTU120, and SYSU) show that the proposed model trained using Deep Action Stamps produce considerable improvements in the action recognition accuracy with less computational cost compared to the state-of-the-art methods.