Activity recognition using Grey-Markov model

Activity Recognition (AR) is a process of identifying actions and goals of one or more agents of interest. AR techniques have been applied to both large and small scale activity identification. Examples of AR techniques include Genetic Algorithm, Markov Chain, and so on. This research proposes a novel method, Grey Markov Model (GMM), for detection and prediction of pre-defined activities. There were three objectives of this research. The first objective was to establish a database of pre-defined human activities. The second objective was to establish the Grey Markov Model. The final objective was to verify the model performance using the established database. This thesis describes the methodology of test setup and data collection, as well as the procedures of model generation. Furthermore, experimental results of model performance verification test are also reported