DEEP LEARNING-BASED TRACKING OF MULTIPLE OBJECTS IN THE CONTEXT OF FARM ANIMAL ETHOLOGY

Abstract

Automatic detection and tracking of individual animals is important to enhance their welfare and to improve our understanding of their behaviour. Due to methodological difficulties, especially in the context of poultry tracking, it is a challenging task to automatically recognise and track individual animals. Those difficulties can be, for example, the similarity of animals of the same species which makes distinguishing between them harder, or sudden changes in their body shape which may happen due to putting on or spreading out the wings in a very short period of time. In this paper, an automatic poultry tracking algorithm is proposed. This algorithm is based on the well-known tracktor approach and tackles multi-object tracking by exploiting the regression head of the Faster R-CNN model to perform temporal realignment of object bounding boxes. Additionally, we use a multi-scale re-identification model to improve the re-association of the detected animals. For evaluating the performance of the proposed method in this study, a novel dataset consisting of seven image sequences that show chicks in an average pen farm in different stages of growth is used.