Identifying quadruped gait in wildlife video

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

14 Citations (Scopus)


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.
Translated title of the contributionIdentifying quadruped gait in wildlife video
Original languageEnglish
Title of host publicationIEEE International Conference on Image Processing, Genova, 11-14 September
PublisherIEEE Computer Society
Pages713 - 716
Number of pages4
ISBN (Print)0780391357
Publication statusPublished - Sept 2005

Bibliographical note

Conference Organiser: IEEE Computer Society


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