  Object detection and classification using video is necessary for intelligent
planning and navigation on a mobile robot. However, current methods can be too
slow or not sufficient for distinguishing multiple classes. Techniques that
rely on binary (foreground/background) labels incorrectly identify areas with
multiple overlapping objects as single segment. We propose two Hierarchical
Markov Random Field models in efforts to distinguish connected objects using
tiered, binary label sets. Near-realtime performance has been achieved using
efficient optimization methods which runs up to 11 frames per second on a dual
core 2.2 Ghz processor. Evaluation of both models is done using footage taken
from a robot obstacle course at the 2010 Intelligent Ground Vehicle
Competition.
