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    A smith-waterman local sequence alignment approach to spatial activity recognition

    Access Status
    Fulltext not available
    Authors
    Riedel, Daniel
    Venkatesh, Svetha
    Liu, Wan-Quan
    Date
    2006
    Type
    Conference Paper
    
    Metadata
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    Citation
    Riedel, 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.
    Source Title
    Proceedings IEEE International Conference on Video and Signal Based Surveillance
    Source Conference
    IEEE International Conference on Video and Signal Based Surveillance 2006
    DOI
    10.1109/AVSS.2006.13
    ISBN
    978-0-7695-2688-1
    School
    Department of Computing
    URI
    http://hdl.handle.net/20.500.11937/22473
    Collection
    • Curtin Research Publications
    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.

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