Detecting lameness using ‘Re-sampling Condensation’ and ‘multi-stream cyclic hidden Markov models’

A system for the tracking and classification of livestock movements is presented. The combined ‘tracker-classifier’ scheme is based on a variant of Isard and Blakes ‘Condensation’ algorithm [Int. J. Comput. Vision (1998) 5] known as ‘Re-sampling Condensation’ 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 incorporate 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 distribution model [Proc. Br. Mach. Vision Conf. (1992) 9] is used to model shape variation accurately. Results are presented that show this algorithm gives improved tracking performance, 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 manner such that the model best representing the data will be propagated through the image sequence.