State Filtering and Change Detection Using TBM Conflict Application to Human Action Recognition in Athletics Videos

In this paper, we propose a tool called temporal credal filter with conflict-based model change (TCF-CMC) to smooth belief functions online in transferable belief model (TBM) framework. The TCF-CMC takes temporal aspects of belief functions into account and relies on conflict information explicitly...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems for video technology 2007-07, Vol.17 (7), p.944-949
Main Authors: Ramasso, E.., Rombaut, M., Pellerin, D..
Format: Article
Language:English
Subjects:
Applied sciences
Belief state filtering
Cameras
Computer Science
Computer Vision and Pattern Recognition
Detection, estimation, filtering, equalization, prediction
Dynamical Systems
Engineering Sciences
Exact sciences and technology
Filtering
Filters
Hidden Markov models
human motion analysis
Humans
Image processing
Image recognition
Information, signal and communications theory
Mathematics
Miscellaneous
Motion analysis
moving camera
Multimedia
novelty detection
Signal and communications theory
Signal and Image processing
Signal processing
Signal, noise
System testing
Telecommunications and information theory
transferable belief model
Uncertainty
Videos
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Summary:In this paper, we propose a tool called temporal credal filter with conflict-based model change (TCF-CMC) to smooth belief functions online in transferable belief model (TBM) framework. The TCF-CMC takes temporal aspects of belief functions into account and relies on conflict information explicitly modelled in TBM when combining beliefs. TBM fusion, in addition to uncertainty, takes into account imprecision and conflict inherent to features. The TCF-CMC takes part in a wider system for human action recognition in videos. The whole system is tested on 62 videos (11000 images) with moving camera and real conditions where the TCF-CMC improves running, jumping, falling and standing-up actions recognition in high jump, pole vault, long jump and triple jump activities. The TCF-CMC is also compared to hidden Markov models. Lastly, a TBM rules-based modelling is compared to Gaussian mixture.
ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2007.896652