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231223s2006 xx ||||| 00| ||eng c |
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|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Dockstader, Shiloh L
|e verfasserin
|4 aut
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| 245 |
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|a Prediction for human motion tracking failures
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|c 2006
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| 336 |
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|a Text
|b txt
|2 rdacontent
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| 337 |
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|a ohne Hilfsmittel zu benutzen
|b n
|2 rdamedia
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| 338 |
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|a Band
|b nc
|2 rdacarrier
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| 500 |
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|a Date Completed 14.03.2006
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|a Date Revised 10.12.2019
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|a published: Print
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|a Citation Status MEDLINE
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| 520 |
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|a We propose a new and effective method of predicting tracking failures and apply it to the robust analysis of gait and human motion. We define a tracking failure as an event and describe its temporal characteristics using a hidden Markov model (HMM). We represent the human body using a three-dimensional, multicomponent structural model, where each component is designed to independently allow the extraction of certain gait variables. To enable a fault-tolerant tracking and feature extraction system, we introduce a single HMM for each element of the structural model, trained on previous examples of tracking failures. The algorithm derives vector observations for each Markov model using the time-varying noise covariance matrices of the structural model parameters. When transformed with a logarithmic function, the conditional output probability of each HMM is shown to have a causal relationship with imminent tracking failures. We demonstrate the effectiveness of the proposed approach on a variety of multiview video sequences of complex human motion
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|a Evaluation Study
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| 650 |
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|a Journal Article
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| 700 |
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|a Imennov, Nikita S
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1997
|g 15(2006), 2 vom: 30. Feb., Seite 411-21
|w (DE-627)NLM09821456X
|x 1057-7149
|7 nnns
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| 773 |
1 |
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|g volume:15
|g year:2006
|g number:2
|g day:30
|g month:02
|g pages:411-21
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|a AR
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| 952 |
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|d 15
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|h 411-21
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