Brunel University Research Archive (BURA) >
University >
Publications >

Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/5961

Title: Descriptive temporal template features for visual motion recognition
Authors: Meng, H
Pears, N
Keywords: Gesture recognition
Event recognition
Embedded vision
Motion analysis
Machine learning
Publication Date: 2009
Citation: Pattern Recognition Letters, 30(12): 1049 - 1058, 2009
Abstract: In this paper, a human action recognition system is proposed. The system is based on new, descriptive ‘temporal template’ features in order to achieve high-speed recognition in real-time, embedded applications. The limitations of the well-known ‘Motion History Image’ (MHI) temporal template are addressed and a new ‘Motion History Histogram’ (MHH) feature is proposed to capture more motion information in the video. MHH not only provides rich motion information, but also remains computationally inexpensive. To further improve classification performance, we combine both MHI and MHH into a low dimensional feature vector which is processed by a support vector machine (SVM). Experimental results show that our new representation can achieve a significant improvement in the performance of human action recognition over existing comparable methods, which use 2D temporal template based representations.
Description: Copyright © 2009 Elsevier B.V. All rights reserved.
URI: http://www.sciencedirect.com/science/article/pii/S0167865509000476
http://bura.brunel.ac.uk/handle/2438/5961
DOI: http://dx.doi.org/10.1016/j.patrec.2009.03.003
ISSN: 0167-8655
Appears in Collections:School of Engineering and Design Research papers
Electronic and Computer Engineering
Publications

Files in This Item:

File Description SizeFormat
Preprint.pdf1.04 MBAdobe PDFView/Open

Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.

 


Library (c) Brunel University.    Powered By: DSpace
Send us your
Feedback. Last Updated: September 14, 2010.
Managed by:
Hassan Bhuiyan