Quick, accurate, smart: 3D computer vision technology helps assessing confined animals’ behaviour

Behavioural indicators are among the preferred parameters to assess welfare. However, behavioural recording (usually from video) can be very time consuming and the accuracy and reliability of the output rely on the experience and background of the observers. The outburst of new video technology and computer image processing gives the basis for promising solutions. Researchers developed a new prototype software able to automatically infer the behaviour of dogs housed in kennels from 3D visual data and through structured machine learning frameworks. The main innovation of the software is its ability to automatically cluster frequently observed temporal patterns of movement without any pre-set ethogram. Conversely, when common patterns are defined through training, the software could detect a deviation from normal behaviour in time or between individuals. The computer vision technique applied to this software is innovative in non-human animal behaviour science. An automatic behaviour recognition system, independent from human subjectivity, could add scientific knowledge on animals’ quality of life in confinement as well as saving time and resources. This 3D framework was designed to be invariant to the dog’s shape and size and could be extended to farm, laboratory and zoo quadrupeds in artificial housing.


Journal Article: https://www.ncbi.nlm.nih.gov/pubmed/27415814


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