Fault-tolerant tracking for gait analysis

Abstract

This research introduces a method of predicting tracking failures and applies it to the robust analysis of human gait. The body is represented using a multicomponent structural model. For each component, the proposed approach extracts features from tracked noise covariance matrices and uses them to construct an observation sequence for a hidden Markov model (HMM) trained to detect tracking failures. When transformed with a logarithmic function, the conditional output probability of the HMM is shown to have a causal relationship with imminent tracking failures. This fusion of multiple structural models with a reliable means of failure prediction facilitates the successful tracking and extraction of gait variables. Results are demonstrated on numerous video sequences.