Deep learning-based human activity analysis for aerial images

Han Yang Wang, Ya Ching Chang, Yi Yu Hsieh, Hua Tsung Chen, Jen-Hui Chuang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Due to the advantages of high mobility and the ability to fly in the sky, drone has inspired more and more applications in recent years. On the other hand, deep learning-based human activity analysis is an important research topic in security surveillance; however, there are few research works on such analysis with aerial images so far. Because of perspective projection, people in aerial images look tilted, which would degrade the performance of human activity analysis. In order to cope with the issue of perspective projection for aerial images, we modify the CNN architecture of a state-of-the-art object detection method, YOLOv2 [12], and build an aerial image dataset with a drone for new model training. Finally, a post-processing method is proposed to classify the pose of a detected person as normal or abnormal, so that the task of human activity analysis with aerial images can be accomplished.

Original languageEnglish
Title of host publication2017 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages713-718
Number of pages6
ISBN (Electronic)9781538621592
DOIs
StatePublished - 19 Jan 2018
Event25th International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2017 - Xiamen, China
Duration: 6 Nov 20179 Nov 2017

Publication series

Name2017 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2017 - Proceedings
Volume2018-January

Conference

Conference25th International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2017
CountryChina
CityXiamen
Period6/11/179/11/17

Keywords

  • Deep learning
  • drone
  • human activity analysis
  • human detection
  • image processing

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