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Bag of Class Posteriors, a new multivariate time series classifier applied to animal behaviour identification
Class Posteriors (BOCP) and the Bag of Class Posterior with Ordering (BOCPO). The models propose a new multi-scale feature representation where the class posterior estimates of contiguous local patterns are aggregated over longer time scales. The models are employed as part of an animal behaviour monitoring system that are comprised of sensors, which are fitted to the animals, and a classifier that translates sensor data into knowledge of the animal’s behaviour.
Animal monitoring systems are commonly developed to infer a small number of behaviours with relevance to a specific application. To investigate if a standard monitoring system with an Inertial Measurement Unit (IMU) can be reused for different management applications, a set of ten cattle behaviours relevant to different management applications were classified with the proposed models. Results indicate that the multi-scale BOCP and BOCPO models were far more capable of classifying the cow behaviours offering a 43% to 77% improvement over benchmark time interval classifiers with fixed time resolution. In addition, the BOCPO model was shown to offer a far more efficient feature representation than the related multi-scale Bag of Features (BOF) classifier (up to 200 times smaller) making it better suited to deploy upon monitoring devices fitted to animals in the field.
History
Publication title
Expert Systems With ApplicationsVolume
42Issue
7Pagination
3774-3784ISSN
0957-4174Department/School
Tasmanian Institute of Agriculture (TIA)Publisher
Pergamon-Elsevier Science LtdPlace of publication
The Boulevard, Langford Lane, Kidlington, Oxford, England, Ox5 1GbRights statement
Copyright 2014 Elsevier LtdRepository Status
- Restricted