We propose a new and effective method of predicting tracking failures and apply it to the robust 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). We represent the human body using a three-dimensional, multicomponent structural model, where each component is designed to independently allow the extraction of certain gait variables. To enable a fault-tolerant tracking and feature extraction system, we introduce a single HMM for each element of the structural model, trained on previous examples of tracking failures. The algorithm derives vector observations for each Markov model using the time-varying noise covariance matrices of the structural model parameters. When transformed with a logarithmic function, the conditional output probability of each HMM is shown to have a causal relationship with imminent tracking failures. We demonstrate the effectiveness of the proposed approach on a variety of multiview video sequences of complex human motion.

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.

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.

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.

We introduce a new approach to motion-based recognition that combines the temporally descriptive abilities of a hidden Markov model (HMM) with the inferential power of a Bayesian belief network. We define activities using a collection of multiple Markov models, each associated with a unique set of body model parameters or gait variables. A single Bayesian network integrates the models by operating on virtual evidence derived from the HMM conditional output probabilities. We introduce both fundamental and auxiliary models for characterizing events and tracking failures, respectively. We demonstrate the system using multi-view video sequences corrupted by occlusion, noise, and entirely missing observations.