Prediction for human motion tracking failures.
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.
ITT Industries Space Systems Division, Rochester, NY 14653-7225, USA. dockstad@ieee.org
Shiloh L
SL
Dockstader
Nikita S
NS
Imennov
D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D005684:Gait / Q000502:physiology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D016247:Information Storage and Retrieval / Q000379:methods; D009068:Movement / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D051598:Whole Body Imaging / Q000379:methods*