A Lightweight Action Recognition Method for Unmanned-Aerial-Vehicle Video

Meng Ding, Ning Li*, Ziang Song, Ruixing Zhang, Xiaxia Zhang, Huiyu Zhou

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In recent year, due to motility and wide coverage, unmanned aerial vehicle (UAV) has been widely applied in surveillance system. Human action recognition in UAV video is essential for surveillance video understanding. However, existing action recognition methods suffer from heavy computing, which makes it hard to deploy in real applications. In this paper, a lightweight action recognition method for UAV video(LARMUV) is proposed. This method is based on TSN and adopt Mo-bileNetV3 as backbone, which greatly reduces amount of computing and parameters. Self-attention mechanism is adopted to capture temporal structure among different frames. For loss function, Focal Loss is used to putting more focus on hard, misclassified examples. Last but not least, knowledge distillation is employed to enhance the performance of our model, which transfer knowledge from a larger teacher model to student model. Experimental results on HMDB51, UCF101 and UAV dataset show that our method can achieve competitive performance compared to baseline methods while run in real-time mode.

Original languageEnglish
Title of host publication2020 IEEE 3rd International Conference on Electronics and Communication Engineering, ICECE 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages181-185
Number of pages5
ISBN (Electronic)9781728192581
DOIs
Publication statusPublished - 17 Feb 2021
Externally publishedYes
Event3rd IEEE International Conference on Electronics and Communication Engineering, ICECE 2020 - Virtual, Xi'an, China
Duration: 14 Dec 202016 Dec 2020

Publication series

Name2020 IEEE 3rd International Conference on Electronics and Communication Engineering, ICECE 2020

Conference

Conference3rd IEEE International Conference on Electronics and Communication Engineering, ICECE 2020
CountryChina
CityVirtual, Xi'an
Period14/12/202016/12/2020

Bibliographical note

Funding Information:
This work received support from Science and Technology on Electro-optic Control Laboratory and Aviation Science Foundation Project (ASFC-20175152036) and Key Project on Artificial intelligence(1004-56XZA19008). The authors are also grateful for the support of their colleagues at the Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education.

Publisher Copyright:
© 2020 IEEE.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

  • action recognition
  • MobileNetV3
  • self-attention
  • UAV

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Electrical and Electronic Engineering

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