Development of a real-time computer vision system for tracking loose-housed pigs

Changes in regulations for livestock animals will in the near future call for loose-house pig breeding systems. These new systems will increase the workload for the farmers, as location and identification of animals will require more time than before. This paper presents a real-time computer vision system for tracking of pigs in loose-housed stables. The system will ease the workload for farmers in identification and locating individual animals. The system consists of a camera and a PC. The PC runs a tracking-algorithm, estimating the positions and identities of the pigs. The tracking algorithm operates in 2 steps. The first step builds up support maps, pointing to preliminary pig segments in each video frame. In the second step the support map segments are used to build up a 5D-Gaussian model of the individual pigs (i.e. position and shape). The system has software correction for fisheye distortion coming from the camera lens. The fisheye lens allows the camera to monitor a much larger area in the stable. The algorithms are developed in MatLab, implemented in C and runs in real-time. Experiments in the lab and in the stable demonstrate the robustness of the system. The system can track at least 3 pigs over a longer time span (more than 8 min) without loosing track and identity of the individual pigs in a realistic experiment.

Research highlights
► Development of a camera based computer vision system for tracking the loose housed pigs.
► The advantage of the solution is that it is not necessary to mount expensive, heavy and battery consuming RFIDs on the pigs.
► Another advantage is that it is a non-contact solution.
► The aim is to track the spatial positions of large numbers of pigs in real time.