A human action recognition system for embedded computer vision application

Hongying Meng, Nick Pears, Chris Bailey

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

Abstract

In this paper, we propose a human action recognition system suitable for embedded computer vision applications in security systems, human-computer interaction and intelligent environments. Our system is suitable for embedded computer vision application based on three reasons. Firstly, the system was based on a linear Support Vector Machine (SVM) classifier where classification progress can be implemented easily and quickly in embedded hardware. Secondly, we use compacted motion features easily obtained from videos. We address the limitations of the well known Motion History Image (MHI) and propose a new Hierarchical Motion History Histogram (HMHH) feature to represent the motion information. HMHH not only provides rich motion information, but also remains computationally inexpensive. Finally, we combine MHI and HMHH together and extract a low dimension feature vector to be used in the SVM classifiers. Experimental results show that our system achieves significant improvement on the recognition performance.

Original languageEnglish
Title of host publication2007 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, VOLS 1-8
Place of PublicationNEW YORK
PublisherIEEE
Pages3213-3218
Number of pages6
ISBN (Print)978-1-4244-1179-5
Publication statusPublished - 2007
EventIEEE Conference on Computer Vision and Pattern Recognition - Minneapolis
Duration: 17 Jun 200722 Jun 2007

Conference

ConferenceIEEE Conference on Computer Vision and Pattern Recognition
CityMinneapolis
Period17/06/0722/06/07

Keywords

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