Publication:
Markov-based failure prediction for human motion analysis

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KU Authors

Co-Authors

Imennov, Nikita S.

Advisor

Publication Date

2003

Language

English

Type

Conference proceeding

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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

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