Online Learning for Beta-Liouville Hidden Markov Models: Incremental Variational Learning for Video Surveillance and Action Recognition

Challenges in realtime installation of surveillance systems is an active area of research, especially with the use of adaptable machine learning techniques. In this paper, we propose the use of variational learning of Beta-Liouville (BL) hidden Markov models (HMM) for AR in an online setup. This proposed incremental framework enables continuous adjustment of the system for better modelling. We evaluate the proposed model on the visible IOSB dataset to validate the framework.