Publication: Markov-based failure prediction for human motion analysis
Program
KU-Authors
KU Authors
Co-Authors
Imennov, Nikita S.
Advisor
Publication Date
2003
Language
English
Type
Conference proceeding
Journal Title
Journal ISSN
Volume Title
Abstract
This paper presents a new method of detecting and predicting motion tracking failures with applications in human motion and gait analysis. We define a tracking failure as an event and describe its temporal characteristics using a hidden Markov model (HMM). This stochastic model is trained using previous examples of tracking failures. We derive vector observations for the HMM using the noise covariance matrices characterizing a tracked, 3-D structural model of the human body. We show a causal relationship between the conditional output probability of the HMM, as transformed using a logarithmic mapping function, and impending tracking failures. Results are illustrated on several multi-view sequences of complex human motion.
Description
Source:
Proceedings of the IEEE International Conference on Computer Vision
Publisher:
IEEE
Keywords:
Subject
Electrical electronics engineering