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Chicken well-being is important for ensuring food security and betternutrition for a growing global human population. In this research, we representbehavior and posture as a metric to measure chicken well-being. With theobjective of detecting chicken posture and behavior in a pen, we employ twoalgorithms: Mask R-CNN for instance segmentation and YOLOv4 in combination withResNet50 for classification. Our results indicate a weighted F1 score of 88.46%for posture and behavior detection using Mask R-CNN and an average of 91%accuracy in behavior detection and 86.5% average accuracy in posture detectionusing YOLOv4. These experiments are conducted under uncontrolled scenarios forboth posture and behavior measurements. These metrics establish a strongfoundation to obtain a decent indication of individual and group behaviors andpostures. Such outcomes would help improve the overall well-being of thechickens. The dataset used in this research is collected in-house and will bemade public after the publication as it would serve as a very useful resourcefor future research. To the best of our knowledge no other research work hasbeen conducted in this specific setup used for this work involving multiplebehaviors and postures simultaneously.

Birds' Eye View: Measuring Behavior and Posture of Chickens as a Metric for Their Well-Being