Markov-Based Failure Prediction for Human Motion Analysis
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, 3D 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.
Cite
Text
Dockstader et al. "Markov-Based Failure Prediction for Human Motion Analysis." IEEE/CVF International Conference on Computer Vision, 2003. doi:10.1109/ICCV.2003.1238638Markdown
[Dockstader et al. "Markov-Based Failure Prediction for Human Motion Analysis." IEEE/CVF International Conference on Computer Vision, 2003.](https://mlanthology.org/iccv/2003/dockstader2003iccv-markov/) doi:10.1109/ICCV.2003.1238638BibTeX
@inproceedings{dockstader2003iccv-markov,
title = {{Markov-Based Failure Prediction for Human Motion Analysis}},
author = {Dockstader, Shiloh L. and Imennov, Nikita S. and Tekalp, A. Murat},
booktitle = {IEEE/CVF International Conference on Computer Vision},
year = {2003},
pages = {1283-1288},
doi = {10.1109/ICCV.2003.1238638},
url = {https://mlanthology.org/iccv/2003/dockstader2003iccv-markov/}
}