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AVSS
2006
IEEE

A Smith-Waterman Local Alignment Approach for Spatial Activity Recognition

13 years 9 months ago
A Smith-Waterman Local Alignment Approach for 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 recognition as the approach is robust to noise and can accomodate 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 ...
Daniel E. Riedel, Svetha Venkatesh, Wanquan Liu
Added 10 Jun 2010
Updated 10 Jun 2010
Type Conference
Year 2006
Where AVSS
Authors Daniel E. Riedel, Svetha Venkatesh, Wanquan Liu
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