A COMPUTER VISION METHOD FOR ON-LINE BEHAVIORAL QUANTIFICATION OF INDIVIDUALLY CAGED POULTRY

摘要

In addition to production, physiology, and health, behavior is an important issue with respect to animal welfare when evaluating novel housing systems. Behavioral characteristics are usually evaluated by audio-visual observation done by a human observer present on the scene. This method is time consuming, expensive, subjective, and prone to human error. Automated objective surveillance, by means of inexpensive cameras and image-processing techniques, has the ability to generate data that provide an objective measure of behavior, without disturbing the animals. The specific purpose of this study was to develop a fully automatic on-line image-processing technique to quantify the behavior of a single laying hen as opposed to the current human visual observation. The image-processing system is based on the principle that the classification of behavior can be translated into classification of time series of different postures of the hen. The hen's postures can be recognized in the camera image. The classification of the hen's behavior is performed by dynamic analysis of a set of measurable parameters, which are calculated from the images using image-processing techniques. The parameters were chosen based on their computational demands and analysis of their discriminative power regarding the different types of a specific behavior. A first implementation of the system allowed us to identify three different types of individual behavior (standing, walking, and scratching). The objective of further investigation will be the classification of up to 15 different types of behavior, such as pecking, eating, drinking, wing stretching, etc