Recognising online spatial activities using a bioinformatics inspired sequence alignment approach

  • Authors:

  • Daniel E. Riedel;Svetha Venkatesh;Wanquan Liu

  • Affiliations:

  • Department of Computing, Curtin University of Technology, GPO Box U1987 Perth, Western Australia 6845, Australia;Department of Computing, Curtin University of Technology, GPO Box U1987 Perth, Western Australia 6845, Australia;Department of Computing, Curtin University of Technology, GPO Box U1987 Perth, Western Australia 6845, Australia

  • Venue:
  • Pattern Recognition
  • Year:
  • 2008

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Abstract

In this paper we address the problem of recognising embedded activities within continuous spatial sequences obtained from an online video tracking system. Traditionally, continuous data streams such as video tracking data are buffered with a sliding window applied to the buffered data stream for activity detection. We introduce an algorithm based on Smith-Waterman (SW) local alignment from the field of bioinformatics that can locate and accurately quantify embedded activities within a windowed sequence. The modified SW approach utilises dynamic programming with two dimensional spatial data to quantify sequence similarity and is capable of recognising sequences containing gaps and significant amounts of noise. A more efficient SW formulation for online recognition, called Online SW (OSW), is also developed. Through experimentation we show that the OSW algorithm can accurately and robustly recognise manually segmented activity sequences as well as embedded sequences from an online tracking system. To benchmark the classification performance of OSW we compare the approach to dynamic time warping (DTW) and the discrete hidden Markov model (HMM). Results demonstrate that OSW produces higher precision and recall than both DTW and the HMM in an online recognition context. With accurately segmented sequences the SW approach produces results comparable to DTW and superior to the HMM. Finally, we confirm the robust property of the SW approach by evaluating it with sequences containing artificially incorporated noise.