  In this paper, we propose a video analytics system to identify the behavior
of turkeys. Turkey behavior provides evidence to assess turkey welfare, which
can be negatively impacted by uncomfortable ambient temperature and various
diseases. In particular, healthy and sick turkeys behave differently in terms
of the duration and frequency of activities such as eating, drinking, preening,
and aggressive interactions. Our system incorporates recent advances in object
detection and tracking to automate the process of identifying and analyzing
turkey behavior captured by commercial grade cameras. We combine deep-learning
and traditional image processing methods to address challenges in this
practical agricultural problem. Our system also includes a web-based user
interface to create visualization of automated analysis results. Together, we
provide an improved tool for turkey researchers to assess turkey welfare
without the time-consuming and labor-intensive manual inspection.
