Markov-Based Failure Prediction for Human Motion Analysis

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.

• Type: Conference
• Year: 2003
• Where: ICCV
• Authors: Shiloh L. Dockstader, Nikita S. Imennov, A. Murat Tekalp
• Keywords: Computer Vision, Human Motion, ICCV 2003, Motion Tracking Failures, Tracking Failures