A smith-waterman local sequence alignment approach to spatial activity recognition
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 recognitio...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE Computer Society Conference Publishing Services
2006
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| Online Access: | http://hdl.handle.net/20.500.11937/22473 |
| _version_ | 1848750879790858240 |
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| author | Riedel, Daniel Venkatesh, Svetha Liu, Wan-Quan |
| author2 | M. Piccardi |
| author_facet | M. Piccardi Riedel, Daniel Venkatesh, Svetha Liu, Wan-Quan |
| author_sort | Riedel, Daniel |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T07:43:51Z |
| format | Conference Paper |
| id | curtin-20.500.11937-22473 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:43:51Z |
| publishDate | 2006 |
| publisher | IEEE Computer Society Conference Publishing Services |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-224732023-02-27T07:34:28Z A smith-waterman local sequence alignment approach to spatial activity recognition Riedel, Daniel Venkatesh, Svetha Liu, Wan-Quan M. Piccardi T. Hintz I. Pavlidis C. Regazzoni X. He 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. 2006 Conference Paper http://hdl.handle.net/20.500.11937/22473 10.1109/AVSS.2006.13 IEEE Computer Society Conference Publishing Services restricted |
| spellingShingle | Riedel, Daniel Venkatesh, Svetha Liu, Wan-Quan A smith-waterman local sequence alignment approach to spatial activity recognition |
| title | A smith-waterman local sequence alignment approach to spatial activity recognition |
| title_full | A smith-waterman local sequence alignment approach to spatial activity recognition |
| title_fullStr | A smith-waterman local sequence alignment approach to spatial activity recognition |
| title_full_unstemmed | A smith-waterman local sequence alignment approach to spatial activity recognition |
| title_short | A smith-waterman local sequence alignment approach to spatial activity recognition |
| title_sort | smith-waterman local sequence alignment approach to spatial activity recognition |
| url | http://hdl.handle.net/20.500.11937/22473 |