The purpose of this study is to investigate the feasibility and validity of an automated image processing method to detect the activity status of pigs. Top-view video images were captured for forty piglets, housed ten per pen. Each pen was monitored by a top-view CCD camera. The image analysis protocol to automatically quantify activity consisted of several steps. First, in order to localise the pigs, ellipse fitting algorithms were employed. Subsequently, activity was calculated by subtracting image background and comparing binarised images. To validate the results, they were compared to labelled behavioural data ('active' versus 'inactive'). This is the first study to show that activity status of pigs in a group can be determined using image analysis with an accuracy of 89.8 %. Since activity status is known to be associated with issues such as lameness, careful monitoring can give an accurate indication of the health and welfare of pigs.
['Mohammad Amin Kashiha', 'Claudia Bahr', 'Sanne Ott', 'Christel Moons', 'Theo Niewold', 'Frank Tuyttens', 'Daniel Berckmans']
Automatic Monitoring of Pig Activity Using Image Analysis