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dc.contributor.authorRiedel, Daniel
dc.contributor.authorVenkatesh, Svetha
dc.contributor.authorLiu, Wan-Quan
dc.contributor.editorM. Piccardi, T. Hintz, I. Pavlidis, C. Regazzoni, X. He
dc.date.accessioned2017-01-30T12:31:36Z
dc.date.available2017-01-30T12:31:36Z
dc.date.created2014-10-28T02:31:41Z
dc.date.issued2006
dc.identifier.citationRiedel, D. and Venkatesh, S. and Liu, W. 2006. A smith-waterman local sequence alignment approach to spatial activity recognition, in M. Piccardi, T. Hintz, I. Pavlidis, C. Regazzoni, X. He (ed), IEEE International Conference on Video and Signal Based Surveillance, Noc 22-24 2006, pp. 54. Sydney, Australia: IEEE Computer Society Conference Publishing Services.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/22473
dc.identifier.doi10.1109/AVSS.2006.13
dc.description.abstract

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.

dc.publisherIEEE Computer Society Conference Publishing Services
dc.titleA smith-waterman local sequence alignment approach to spatial activity recognition
dc.typeConference Paper
dcterms.source.startPage54
dcterms.source.endPage54
dcterms.source.titleProceedings IEEE International Conference on Video and Signal Based Surveillance
dcterms.source.seriesProceedings IEEE International Conference on Video and Signal Based Surveillance
dcterms.source.isbn978-0-7695-2688-1
dcterms.source.conferenceIEEE International Conference on Video and Signal Based Surveillance 2006
dcterms.source.conference-start-dateDec 4 2006
dcterms.source.conferencelocationSydney, Australia
dcterms.source.placeSydney, Australia
curtin.departmentDepartment of Computing
curtin.accessStatusFulltext not available


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