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.
|Translated title of the contribution||Recognizing Animals Using Motion Parts|
|Title of host publication||Unknown|
|Pages||796 - 808|
|Number of pages||12|
|Publication status||Published - Sep 2005|