Turkey Behavior Identification Using Video Analytics And Object Tracking

In this paper, we propose a method to identify behavior of experimental turkeys by automatically analyzing video recordings. Monitoring turkey health during production is crucial for improved turkey production. Turkey health can be reflected through their common behavior, and changes in the frequency and duration of their behavior can be used to detect sick turkeys early. Video recordings can be manually annotated to assist identifying turkey behaviors, but this is both time consuming and labor intensive. In this paper, we monitor and detect changes in turkey behavior using video analytics. Behaviors of interest include eating, drinking, preening, and pecking. Identifying these behaviors requires accurate estimates of turkeys' and turkey heads' locations. Re-identification of each turkey is crucial after significant shape deformation such as wing flapping and fast walking. Therefore, our system integrates a state-of-the-art turkey tracker and a head tracker with a behavior identification module to identify turkey behavior. Results demonstrate that our system is effective and accurate at estimating the spatial location of turkeys and their heads, and identifying all behaviors of interest with high recall.