Detecting Lameness in Livestock using Resampling Condensationand Multi-stream Cyclic Hidden Markov Models

Abstract

A system for the tracking and classification of livestock movements is pre-sented. The combined ‘tracker-classifier ’ scheme is based on a variant of Is-ard and Blakes ‘Condensation ’ algorithm [6] known as ‘Re-sampling Con-densation ’ in which a second set of samples is taken from each image in the input sequence based on the results of the initial Condensation sampling. This is analogous to a single iteration of a genetic algorithm and serves to incor-porate image information in sample location. Re-sampling Condensation relies on the variation within the spatial (shape) model being separated into pseudo-independent components (analogous to genes). In the system a hierarchical spatial model based on a variant of the Point DistributionModel [16] is used to model shape variation accurately. Re-sults are presented that show this algorithm gives improved tracking perfor-mance, with no computational overhead, over Condensation alone. Separate Cyclic Hidden Markov Models are used to model ‘Healthy ’ and ‘Lame ’ movements within the Condensation framework in a competitive man-ner such that the model best representing the data will be propagated through the image sequence. 1