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Recognizing Animals Using Motion Parts

Changming Kong, Andrew Calway, Majid Mirmehdi, Recognizing Animals Using Motion Parts. Proceedings of the 16th British Machine Vision Conference. ISBN 1-901725-29-4, pp. 796–808. September 2005. PDF, 2516 Kbytes.

Abstract

We describe a method for automatically recognizing animals in image sequences based on their distinctive locomotive movement patterns. The 2-D motion field associated with the animal is represented using a "configuration of motion parts" model, the characteristics of which are learned from training data. We adopt an unsupervised approach to learning model parameters, based on minimal a priori knowledge of the physical or locomotive characteristics of the animals concerned. Results are presented demonstrating excellent classification performance, with accuracy exceeding 98% on a test set consisting of over 100 sequences of 7 different species.

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