Quantifying defence cascade responses as indicators of pig affect and welfare using computer vision methods

Abstract

Affective states are key determinants of animal welfare. Assessing such states under field conditions is thus an important goal in animal welfare science. The rapid Defence Cascade (DC) response (startle, freeze) to sudden unexpected stimuli is a potential indicator of animal affect; humans and rodents in negative affective states often show potentiated startle magnitude and freeze duration. To be a practical field welfare indicator, quick and easy measurement is necessary. Here we evaluate whether DC responses can be quantified in pigs using computer vision. 280 video clips of induced DC responses made by 12 pigs were analysed by eye to provide ‘ground truth’ measures of startle magnitude and freeze duration which were also estimated by (i) sparse feature tracking computer vision image analysis of 200 Hz video, (ii) load platform, (iii) Kinect depth camera, and (iv) Kinematic data. Image analysis data strongly predicted ground truth measures and were strongly positively correlated with these and all other estimates of DC responses. Characteristics of the DC-inducing stimulus, pig orientation relative to it, and ‘relaxed-tense’ pig behaviour prior to it moderated DC responses. Computer vision image analysis thus offers a practical approach to measuring pig DC responses, and potentially pig affect and welfare, under field conditions.