A smith-waterman local alignment approach for spatial activity recognition Riedel, Daniel E., Venkatesh, Svetha and Liu, Wanquan 2006, A smith-waterman local alignment approach for spatial activity recognition, in AVSS 2006 : Proceedings of the IEEE International Conference on Video and Signal Based Surveillance, IEEE, [Washington, D. C.], pp. 54-59. Attached Files Name Description MIMEType Size Downloads venkatesh-asmithwaterman-2006.pdf Published version application/pdf 174.60KB 125 Title A smith-waterman local alignment approach for spatial activity recognition Author(s) Riedel, Daniel E. Venkatesh, Svetha orcid.org/0000-0001-8675-6631 Liu, Wanquan Conference name IEEE International Conference on Video and Signal Based Surveillance (2006 : Sydney, N. S. W.) Conference location Sydney, N. S. W. Conference dates 22-24 Nov. 2006 Title of proceedings AVSS 2006 : Proceedings of the IEEE International Conference on Video and Signal Based Surveillance Editor(s) [Unknown] Publication date 2006 Conference series IEEE International Conference on Video and Signal Based Surveillance Start page 54 End page 59 Total pages 6 Publisher IEEE Place of publication [Washington, D. C.] Keyword(s) sequence similarity smith waterman local alignment spatial activity recognition spatial data Summary In this paper we address the spatial activity recognition problem with an algorithm based on Smith-Waterman (SW) local alignment. The proposed SW approach utilises dynamic programming with two dimensional spatial data to quantify sequence similarity. SW is well suited for spatial activity recognition as the approach is robust to noise and can accommodate gaps, resulting from tracking system errors. Unlike other approaches SW is able to locate and quantify activities embedded within extraneous spatial data. Through experimentation with a three class data set, we show that the proposed SW algorithm is capable of recognising accurately and inaccurately segmented spatial sequences. To benchmark the techniques classification performance we compare it to the discrete hidden markov model (HMM). Results show that SW exhibits higher accuracy than the HMM, and also maintains higher classification accuracy with smaller training set sizes. We also confirm the robust property of the SW approach via evaluation with sequences containing artificially introduced noise. Notes This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. ISBN 0769526888 9780769526881 Language eng Field of Research 089999 Information and Computing Sciences not elsewhere classified Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category E1.1 Full written paper - refereed Copyright notice ©2006, IEEE Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30044606 Document type: Conference Paper Collections: School of Information Technology Open Access Collection   Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Fri, 20 Apr 2012, 11:34:55 EST Fri, 27 Apr 2012, 15:04:47 EST Fri, 27 Apr 2012, 15:06:05 EST Fri, 27 Apr 2012, 15:07:01 EST Thu, 03 May 2012, 11:41:47 EST Tue, 28 Jan 2014, 13:39:28 EST Tue, 06 Sep 2016, 18:45:33 EST Tue, 05 Dec 2017, 12:31:31 EST Fri, 07 Sep 2018, 15:52:48 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 2 times in Scopus Search Google Scholar Access Statistics: 338 Abstract Views, 125 File Downloads  -  Detailed Statistics Created: Fri, 20 Apr 2012, 11:34:55 EST

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