Towards Unsupervised Learning, Classification and Prediction of Activities in a Stream-Based Framework
2015 (English)In: Proceedings of the Thirteenth Scandinavian Conference on Artificial Intelligence (SCAI), 2015Conference paper (Refereed)
Learning to recognize common activities such as traffic activities and robot behavior is an important and challenging problem related both to AI and robotics. We propose an unsupervised approach that takes streams of observations of objects as input and learns a probabilistic representation of the observed spatio-temporal activities and their causal relations. The dynamics of the activities are modeled using sparse Gaussian processes and their causal relations using a probabilistic graph. The learned model supports in limited form both estimating the most likely current activity and predicting the most likely future activities. The framework is evaluated by learning activities in a simulated traffic monitoring application and by learning the flight patterns of an autonomous quadcopter.
Place, publisher, year, edition, pages
Online Unsupervised Learning, Activity Recognition, Situation Awareness
IdentifiersURN: urn:nbn:se:liu:diva-121125OAI: oai:DiVA.org:liu-121125DiVA: diva2:852110
13th Scandinavian Conference on Artificial Intelligence
FunderCUGS (National Graduate School in Computer Science)eLLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications