Appearance and motion information based human activity recognition

Mahmoud Al-Faris, John Chiverton, Linda Yang, David Ndzi

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

4 Citations (Scopus)

Abstract

Activity recognition is an essential objective of a smart building system which responds to what is happening in a scene. In this paper, a view invariant activity recognition system is proposed to recognise human actions. Selection of applicable features is made and solutions are proposed to deal with probable challenges including differing views on actions and directionality issues. This paper explores a number of features that can be utilised in action recognition systems and chooses suitable features to mitigate the challenges properly. Motion History Image (MHI) based on historical appearance information is used in combination with local motion vectors which are computed through each iteration sequence of the MHI information using an optical flow algorithm. A multiview dataset (MuHaVi) and a single view dataset (Weizmann) are used to demonstrate and validate the proposed method. Our method, can detect a wide range of actions in multi-view scenarios and shows competitive performance in comparison with state-of-the-art action classification techniques.
Original languageEnglish
Title of host publicationIET 3rd International Conference on ​​Intelligent Signal Processing (ISP 2017)
Place of PublicationLondon
PublisherIET
Number of pages6
ISBN (Electronic)978-1-78561-708-9
ISBN (Print)978-1-78561-707-2
DOIs
Publication statusPublished - 4 Dec 2017
Event​IET 3rd International Conference on ​​Intelligent Signal Processing - Savoy, London, United Kingdom
Duration: 4 Dec 20175 Dec 2017
https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8329306

Conference

Conference​IET 3rd International Conference on ​​Intelligent Signal Processing
Abbreviated titleISP 2017
Country/TerritoryUnited Kingdom
CityLondon
Period4/12/175/12/17
Internet address

Keywords

  • motion history image
  • optical flow

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