In addition to production, physiology, and health, behavior is an important issue with respect to animal welfarewhen evaluating novel housing systems. Behavioral characteristics are usually evaluated by audio-visual observation doneby 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 togenerate data that provide an objective measure of behavior, without disturbing the animals. The specific purpose of this studywas to develop a fully automatic on-line image-processing technique to quantify the behavior of a single laying hen as opposedto the current human visual observation. The image-processing system is based on the principle that the classification ofbehavior can be translated into classification of time series of different postures of the hen. The hens postures can berecognized in the camera image. The classification of the hens behavior is performed by dynamic analysis of a set ofmeasurable parameters, which are calculated from the images using image-processing techniques. The parameters werechosen based on their computational demands and analysis of their discriminative power regarding the different types of aspecific 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 typesof behavior, such as pecking, eating, drinking, wing stretching, etc.
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Leroy et al. (2006) studied this question.