Behavior-induced health condition monitoring of caged chickens using binocular vision

Abstract The behaviors of chicken can reflect its health condition. However, the existing behaviors information acquisition directly depending on human observation is tedious, laborious and inaccurate. This paper aims to present an automatic behaviors information monitoring method for caged chicken using the binocular vision system. First, a 2-Dimension image segmentation algorithm based on active contour model was improved to accurately segment the chicken head and body. Specifically, ‘S’ weight of HSV color model and ‘a’ weight of Lab color model were used as the fundamental images for chicken body and head segmentation, respectively. Binary operation and morphology processing were utilized to obtain the rough segmentation results, which were regarded as the initial contour of the active contour model. In order to eliminate the interference of cage, we improved the active contour model by integrating with gray morphology and Gaussian filter operations. Second, the 3D image reconstruction was carried out using pairs of rectified images obtained from the binocular cameras. Thus, 3D contours of chicken body and head can be extracted from the above 2D segmentation and 3D reconstruction results. Third, motion parameters derived from eating and drinking behaviors of chicken were computed from the 3D space. Finally, the relationship between behaviors information and health condition was explored. The experimental results showed that the proposed method can monitor the health condition of the caged chicken in real-time.