Unsupervised Human Activity Analysis for Intelligent Mobile Robot

The success of intelligent mobile robots operating and collaborating with humans in daily living environments depends on their ability to generalise and learn human movements, and obtain a shared understanding of an observed scene. In this thesis we aim to understand human activities being performed in real-world environments from long-term observation from an autonomous mobile robot. A number of qualitative spatial-temporal representations are used to capture different aspects of the relations between human subjects and their environment. Analogous to information retrieval on text corpora, a generative probabilistic technique is used to recover latent, semantically-meaningful concepts in the encoded observations in an unsupervised manner. The small number of concepts discovered are considered as human activity classes, granting the robot a low-dimensional understanding of visually observed complex scenes. Finally, variational inference is used to facilitate incremental updating of such concepts allowing for efficient learning and updating of human activity models over time, resulting in efficient life-long learning.