Joint wavelet video denoising and motion activity detection in multimodal human activity analysis: application to video-assisted bioacoustic/psychophysiological monitoring

  • Authors:

  • C. A. Dimoulas;K. A. Avdelidis;G. M. Kalliris;G. V. Papanikolaou

  • Affiliations:

  • Laboratory of Electroacoustics and TV Systems, Dept of Elec and Computer Engineering, Laboratory of Electronic Media, Dept of Journalism and Mass Commun, Aristotle Univ of Thessaloniki, Thessaloni ...;Laboratory of Electroacoustics and TV Systems, Dept of Elec and Computer Engineering, Laboratory of Electronic Media, Dept of Journalism and Mass Commun, Aristotle Univ of Thessaloniki, Thessaloni ...;Laboratory of Electroacoustics and TV Systems, Dept of Elec and Computer Engineering, Laboratory of Electronic Media, Dept of Journalism and Mass Commun, Aristotle Univ of Thessaloniki, Thessaloni ...;Laboratory of Electroacoustics and TV Systems, Dept of Elec and Computer Engineering, Laboratory of Electronic Media, Dept of Journalism and Mass Commun, Aristotle Univ of Thessaloniki, Thessaloni ...

  • Venue:
  • EURASIP Journal on Advances in Signal Processing
  • Year:
  • 2008

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Abstract

The current work focuses on the design and implementation of an indoor surveillance application for long-term automated analysis of human activity, in a video-assisted biomedical monitoring system. Video processing is necessary to overcome noise-related problems, caused by suboptimal video capturing conditions, due to poor lighting or even complete darkness during overnight recordings. Modified wavelet-domain spatiotemporal Wiener filtering and motion-detection algorithms are employed to facilitate video enhancement, motion-activity-based indexing and summarization. Structural aspects for validation of the motion detection results are also used. The proposed system has been already deployed in monitoring of long-term abdominal sounds, for surveillance automation, motion-artefacts detection and connection with other psychophysiological parameters. However, it can be used to any video-assisted biomedical monitoring or other surveillance application with similar demands.