Incremental on-line learning of human motion using Gaussian adaptive resonance hidden Markov model
Farhan Dawood, Chu Kiong Loo, Wei Hong Chin · 2013
In this paper we present an approach for on-line and incremental learning of human motion patterns through continuous observation of motion using novel Topological Gaussian Adaptive Resonance Hidden Markov Model (TGART-HMM). The observed human motion patterns are encoded in a novel modified version of Hidden Markov Model (HMM) called TGART-HMM. The on-line learning process consists of updating the structure of Hidden Markov Model using a topology-learning mechanism based on Gaussian Adaptive Resonance Theory (GART). The model size is adaptable based on the observed motion patterns. The resulting HMM structure is a graph where each node represents an encoded motion pattern. The parameters of TGART-HMM are updated incrementally to incorporate incessant motion patterns. The algorithm is tested on motion captured data to test the efficacy of the system.