Stochastic modeling of motion tracking failures

Abstract

This research introduces a new and effective method of predicting motion tracking failures and demonstrates its application towards the 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). This stochastic model is trained using previous examples of tracking failures and is applied to the Kalman-based tracking of a parametric, structural model of the human body. With an observation sequence derived from the noise covariance matrices of the structural model parameters, we show a causal relationship between the conditional output probability of the HMM and imminent tracking failures. Results are demonstrated on a variety of multi-view sequences of complex human motion.