Conditional random fields for activity recognition

Activity recognition is a key component for creating intelligent, multi-agent systems. Intrinsically, activity recognition is a temporal classification problem. In this paper, we compare two models for temporal classification: hidden Markov models (HMMs), which have long been applied to the activity recognition problem, and conditional random fields (CRFs). CRFs are discriminative models for labeling sequences. They condition on the entire observation sequence, which avoids the need for independence assumptions between observations. Conditioning on the observations vastly expands the set of features that can be incorporated into the model without violating its assumptions. Using data from a simulated robot tag domain, chosen because it is multi-agent and produces complex interactions between observations, we explore the differences in performance between the discriminatively trained CRF and the generative HMM. Additionally, we examine the effect of incorporating features which vi...

• Type: Conference
• Year: 2007
• Where: ATAL
• Authors: Douglas L. Vail, Manuela M. Veloso, John D. Lafferty
• Keywords: Activity Recognition, ATAL 2007, Hidden Markov Models, Independence Assumptions