  We present a novel data set made up of omnidirectional video of multiple
objects whose centroid positions are annotated automatically. Omnidirectional
vision is an active field of research focused on the use of spherical imagery
in video analysis and scene understanding, involving tasks such as object
detection, tracking and recognition. Our goal is to provide a large and
consistently annotated video data set that can be used to train and evaluate
new algorithms for these tasks. Here we describe the experimental setup and
software environment used to capture and map the 3D ground truth positions of
multiple objects into the image. Furthermore, we estimate the expected
systematic error on the mapped positions. In addition to final data products,
we release publicly the software tools and raw data necessary to re-calibrate
the camera and/or redo this mapping. The software also provides a simple
framework for comparing the results of standard image annotation tools or
visual tracking systems against our mapped ground truth annotations.
