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 use RANSAC to segment the foreground region. A novel technique, employing normalised convolution is then utilised to interpolate dense flow from the sparsely defined foreground. Dense flow is extracted for a number of clips demonstrating quadruped gait and other movements. Principal component analysis (PCA) is applied to this set of dense flows and eigenvectors not encapsulating periodic internal motion characteristics are disregarded. The projection coefficients for the remaining 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 coefficientsy relative phase differences are deduced using spectral analysis and degree of periodicity using dynamic time warping. These parameters 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.