This paper describes a novel approach to detecting walking quadrupeds in unedited wildlife film footage. Variable lighting, moving backgrounds and camouflaged animals make traditional foreground extraction techniques such as optical flow and background subtraction unstable. We track a sparse set of points over a short film clip and interpolate dense flow, using normalized convolution. Principal component analysis (PCA) is applied to a set of dense flows, describing quadruped gait and other movements. The projection coefficients for relevant principal components are analysed as one dimensional time series. Projection coefficient variation reflects changes in the velocity and relative alignment of the components of the foreground object. These coefficients' relative phase differences are used to train a KNN classifier which segments the training data with 93% success rate. By generating projection coefficients for unseen footage, the system has successfully located examples of quadruped gait previously missed by human observers.