research-article

Part-wise Spatio-temporal Attention Driven CNN-based 3D Human Action Recognition

Authors:

Dinesh Kumar Vishwakarma,

Paras AgarwalAuthors Info & Claims

ACM Transactions on Multimidia Computing Communications and Applications, Volume 17, Issue 3

Article No.: 86, Pages 1 - 24

https://doi.org/10.1145/3441628

Published: 22 July 2021 Publication History

Abstract

Recently, human activity recognition using skeleton data is increasing due to its ease of acquisition and finer shape details. Still, it suffers from a wide range of intra-class variation, inter-class similarity among the actions and view variation due to which extraction of discriminative spatial and temporal features is still a challenging problem. In this regard, we present a novel Residual Inception Attention Driven CNN (RIAC-Net) Network, which visualizes the dynamics of the action in a part-wise manner. The complete skeletonis partitioned into five key parts: Head to Spine, Left Leg, Right Leg, Left Hand, Right Hand. For each part, a Compact Action Skeleton Sequence (CASS) is defined. Part-wise skeleton-based motion dynamics highlights discriminative local features of the skeleton that helps to overcome the challenges of inter-class similarity and intra-class variation with improved recognition performance. The RIAC-Net architecture is inspired by the concept of inception-residual representation that unifies the Attention Driven Residues (ADR) with inception-based Spatio-Temporal Convolution Features (STCF) to learn efficient salient action features. An ablation study is also carried out to analyze the effect of ADR over simple residue-based action representation. The robustness of the proposed framework is evaluated by performing an extensive experiment on four challenging datasets: UT Kinect Action 3D, Florence 3D action, MSR Daily Action3D, and NTU RGB-D datasets, which consistently demonstrate the superiority of the proposed method over other state-of-the-art methods.

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Index Terms

Part-wise Spatio-temporal Attention Driven CNN-based 3D Human Action Recognition
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Activity recognition and understanding

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cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 17, Issue 3

August 2021

443 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3476118

Editor:
Alberto Del Bimbo
University of Firenze, Italy

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Copyright © 2021 Association for Computing Machinery.

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Publication History

Published: 22 July 2021

Accepted: 01 December 2020

Revised: 01 December 2020

Received: 01 July 2020

Published in TOMM Volume 17, Issue 3

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https://doi.org/10.1049/cvi2.70003
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Majcher MKwolek B(2025)Object pose tracking using multimodal knowledge from RGB images and quaternion-based rotation contextsApplied Soft Computing10.1016/j.asoc.2025.112699170(112699)Online publication date: Feb-2025
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