Publicaties
Don’t get lost in the crowd: Graph convolutional network for online animal tracking in dense groups KU Leuven
Tracking the behaviour of animals in group-housed situations is a critical area of study for precision livestock farming, but it poses challenges in diverse and crowded environments. Existing methods often struggle with false positives and false negatives due to the complexities of these scenarios. In this study, we propose a robust computer vision algorithm for long-term (>10mins) animal tracking, with a primary focus on group-housed pigs. ...
Recognizing the rooting action of prepartum sow in free-farrowing pen using computer vision KU Leuven
motivation for nest-building. This behavior includes actions such as rooting, pawing, and foraging. Automatic recognition of these specific actions can be beneficial for identifying this nesting behavior and assessing a sow’s maternal ability, as well as predicting the approach of the delivery time. In this study, an automatic method for rooting action recognition was developed for prepartum sow in opened movable crate farrowing pen using ...
Where's your head at? Detecting the orientation and position of pigs with rotated bounding boxes KU Leuven
Value of simplified lung lesions scoring systems to inform future codes for routine meat inspection in pigs KU Leuven
Background Across the European Union (EU), efforts are being made to achieve modernisation and harmonisation of meat inspection (MI) code systems. Lung lesions were prioritised as important animal based measures at slaughter, but existing standardized protocols are difficult to implement for routine MI. This study aimed to compare the informative value and feasibility of simplified lung lesion scoring systems to inform future codes for routine ...