  Our objective is to locate and provide a unique identifier for each mouse in
a cluttered home-cage environment through time, as a precursor to automated
behaviour recognition for biological research. This is a very challenging
problem due to (i) the lack of distinguishing visual features for each mouse,
and (ii) the close confines of the scene with constant occlusion, making
standard visual tracking approaches unusable. However, a coarse estimate of
each mouse's location is available from a unique RFID implant, so there is the
potential to optimally combine information from (weak) tracking with coarse
information on identity. To achieve our objective, we make the following key
contributions: (a) the formulation of the object identification problem as an
assignment problem (solved using Integer Linear Programming), and (b) a novel
probabilistic model of the affinity between tracklets and RFID data. The latter
is a crucial part of the model, as it provides a principled probabilistic
treatment of object detections given coarse localisation. Our approach achieves
77% accuracy on this animal identification problem, and is able to reject
spurious detections when the animals are hidden.
