  Increasing demand for meat products combined with farm labor shortages has
resulted in a need to develop new real-time solutions to monitor animals
effectively. Significant progress has been made in continuously locating
individual pigs using tracking-by-detection methods. However, these methods
fail for oblong pens because a single fixed camera does not cover the entire
floor at adequate resolution. We address this problem by using multiple
cameras, placed such that the visual fields of adjacent cameras overlap, and
together they span the entire floor. Avoiding breaks in tracking requires
inter-camera handover when a pig crosses from one camera's view into that of an
adjacent camera. We identify the adjacent camera and the shared pig location on
the floor at the handover time using inter-view homography. Our experiments
involve two grow-finish pens, housing 16-17 pigs each, and three RGB cameras.
Our algorithm first detects pigs using a deep learning-based object detection
model (YOLO) and creates their local tracking IDs using a multi-object tracking
algorithm (DeepSORT). We then use inter-camera shared locations to match
multiple views and generate a global ID for each pig that holds throughout
tracking. To evaluate our approach, we provide five two-minutes long video
sequences with fully annotated global identities. We track pigs in a single
camera view with a Multi-Object Tracking Accuracy and Precision of 65.0% and
54.3% respectively and achieve a Camera Handover Accuracy of 74.0%. We
open-source our code and annotated dataset at
https://github.com/AIFARMS/multi-camera-pig-tracking
