Towards re-identification for long-term tracking of group housed pigs

Abstract

Automatic monitoring tools can be useful for assessing the health and welfare status of animals. Specifically, a computer-vision-based tracking tool could be helpful to remotely and automatically monitoring the behaviour of individual animals. However, animals housed in partly covered pens present a particular challenge for animal tracking due to the possibility for animals to disappear from and reappear in the field of view (FOV). The aim of this study was to develop a tracking method for weaner pigs housed in partly covered pens, with the particular aim to re-identify individuals when they reappear in the FOV. In this study a one-shot tracker in which the detection and re-identification (re-ID) branches were jointly trained was adopted for tracking pigs. Three associations based on re-ID features and intersection over union (IoU) were used for matching the correct ID, especially re-identifying individuals reappearing in the FOV. Two sets of short videos were selected to test the model, with a first set of two short videos (mean ± SD: 1m50s ± 20) and a second set of three short videos (mean ± SD: 10m08s ± 3m52s). The model reached the performance of 91.41% and 88.33% in MOTA and IDF1 on the first set of videos, and 81.17% in mean tracking percentage per individual on the second set. The test on one long video (from the same pen, length: 85 m) achieved a tracking percentage of 16.78% per individual. The suggested method improved automatic individual behaviour analysis in complex environments where animals can leave the FOV.