venkatesh-asmithwaterman-2006.pdf (174.6 kB)
A smith-waterman local alignment approach for spatial activity recognition
conference contribution
posted on 2006-01-01, 00:00 authored by D Riedel, Svetha VenkateshSvetha Venkatesh, W LiuIn 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.
History
Event
IEEE International Conference on Video and Signal Based Surveillance (2006 : Sydney, N. S. W.)Pagination
54 - 59Publisher
IEEELocation
Sydney, N. S. W.Place of publication
[Washington, D. C.]Start date
2006-11-22End date
2006-11-24ISBN-13
9780769526881ISBN-10
0769526888Language
engNotes
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E1.1 Full written paper - refereedCopyright notice
2006, IEEETitle of proceedings
AVSS 2006 : Proceedings of the IEEE International Conference on Video and Signal Based SurveillanceUsage metrics
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