Paper Info
Title
State Filtering and Change Detection Using TBM Conflict Application to Human Action Recognition in Athletics Videos
Abstract
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.
Year
DOI
Venue
2007
10.1109/TCSVT.2007.896652
Circuits and Systems for Video Technology, IEEE Transactions
Keywords
Field
DocType
belief networks,smoothing methods,video signal processing,TBM conflict application,athletics videos,change detection,conflict based model change,human action recognition,smooth belief functions online,state filtering,temporal credal filter,transferable belief model,Belief state filtering,human motion analysis,moving camera,novelty detection,transferable belief model
Computer vision,Novelty detection,Change detection,Computer science,Conflict theories,Feature extraction,Artificial intelligence,Transferable belief model,Motion analysis,Jump,Hidden Markov model,Machine learning
Journal
Volume
Issue
ISSN
17
7
1051-8215
Citations 
PageRank 
References 
7
0.63
13
Authors
3
Name
Order
Citations
PageRank
Emmanuel Ramasso118515.13
M. Rombaut2766.90
Pellerin, D.3727.20