Indoor Activity Recognition Using a Hybrid Generative-Discriminative Approach with Hidden Markov Models and Support Vector Machines

Abstract

Human activity recognition is used for many practical applications such as context modeling in smart cities, surveillance and assisted living. In this paper, we apply a hybrid generative-discriminative approach using Fisher kernels with inverted Dirichlet-based and inverted Beta-Liouville-based hidden Markov models (HMMs) to improve the recognition performance. We propose a method that combines HMMs as a generative approach, with the discriminative approach of Support Vector Machine (SVM). This strategy allows us to deal with Spatio-temporal motion data, and at the same time use the special focus on the classification task that SVM could provide us. Experiments on the challenging activity recognition benchmark UCF101, demonstrate an effective improvement of the recognition performance compared to the standard generative and Gaussian-based HMM approaches.