Data-driven robust human action recognition

Jian Liu

Research output: ThesisDoctoral Thesis

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Abstract

Human action recognition has wide applications in security, entertainment, sports, and medicine. It is very challenging to develop robust action recognition models for real-life scenarios, where human movements are associated with numerous irrelevant variations such as viewpoints, backgrounds, and illuminations. This thesis proposes novel data-driven methods to address the fundamental problems in robust action recognition, including human data synthesis, spatio-temporal representation, action classification, and human pose recovery. Besides, the adversarial robustness of human action recognition is investigated in this thesis.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • The University of Western Australia
Thesis sponsors
Award date26 Jan 2020
DOIs
Publication statusUnpublished - 2020

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