  We present a novel approach for estimating the 2D pose of an articulated
object with an application to automated video analysis of small laboratory
animals. We have found that deformable part models developed for humans,
exemplified by the flexible mixture of parts (FMP) model, typically fail on
challenging animal poses. We argue that beyond encoding appearance and spatial
relations, shape is needed to overcome the lack of distinctive landmarks on
laboratory animal bodies. In our approach, a shape consistent FMP (scFMP) model
computes promising pose candidates after a standard FMP model is used to
rapidly discard false part detections. This "cascaded" approach combines the
relative strengths of spatial-relations, appearance and shape representations
and is shown to yield significant improvements over the original FMP model as
well as a representative deep neural network baseline.
