Broiler chicken production is a major agricultural industry, yet it faces persistent challenges related to animal welfare–most notably, lameness caused by selective breeding for rapid growth. Traditional gait assessment methods, such as Kestin’s scoring system, obstacle tests, and latency-to-lie, have been valuable but they are typically limited to single-bird evaluations in controlled environments, require trained personnel, and are slow due to their manual nature. In this work, we introduce MiGa , a multi-chicken gait assessment system that leverages computer vision and machine learning to automatically evaluate the gait of multiple birds simultaneously in more naturalistic settings. Our approach integrates four components: a multi-bird detector, a pose estimator, a tracking module, and a gait-score regressor. To support development and benchmarking, we introduce the GAIT dataset suite , which includes dedicated datasets for detection, pose extraction, tracking, and gait-score prediction. Experimental results demonstrate that MiGa achieves impressive gait scoring accuracy of 92% and detection processing speed of 30FPS. This system enables scalable, automated locomotion assessment in realistic multi-bird scenarios, contributing toward improved welfare monitoring in broiler production. The project page is available at https://uark-aicv.github.io/MiGa/ .
Tran et al. (2026) studied this question.