Quick, Accurate, Smart: 3D Computer Vision Technology Helps Assessing Confined Animals’ Behaviour

Abstract

Mankind directly controls the environment and lifestyles of several domestic species for purposes ranging from production and research to conservation and companionship. These environments and lifestyles may not offer these animals the best quality of life. Behaviour is a direct reflection of how the animal is coping with its environment. Behavioural indicators are thus 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. In this pilot study, we present 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. Depth information acquired through 3D features, body part detection and training are the key elements that allow the machine to recognise postures, trajectories inside the kennel and patterns of movement that can be later labelled at convenience. 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, a deviation from normal behaviour in time or between individuals could be assessed. The software accuracy in correctly detecting the dogs’ behaviour was checked through a validation process. 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. The computer vision technique applied to this software is innovative in non-human animal behaviour science. Further improvements and validation are needed, and future applications and limitations are discussed.

A computer vision for animal ecology

“…Three-dimensional imaging has recently been used to track animal behaviour within large indoor enclosures (e.g. Barnard et al, 2016), and applying these tools to animals in natural landscapes is an developing area of research (Robie et al, 2017).…”

Challenges and solutions for studying collective animal behaviour in the wild

“…For example, fully convolutional networks-relatively new tools from deep learning-appear to be well suited to semantic segmentation of complex images in which objects of interest can have variable size and shape, and be partially occluded [32]. Algorithms that explicitly model body orientation, structure and limb orientation using multi-camera reconstructions [33] or 3D cameras [18,34] also appear promising. These and similar methods will allow researchers to access information about individuals that is not contained in the time series of positions typically collected from tracked field imagery.…”

Image Analysis and Computer Vision Applications in Animal Sciences: An Overview

“…Using this technique associated with a transfer function model, with a single input and single output, Kashiha et al (91) were able to identify pig drinking behavior with an R 2 of 0.92 on a single dataset with 40 pigs divided in 4 pens. Machine learning techniques have proven efficient for the identification of animal posture such as standing, lying, or sitting (85,93). In their study, Barnard et al (85) achieved a mean accuracy of 0.91 when using a structural support vector machine to classify dog postures from depth images.…”