  The key prerequisite for accessing the huge potential of current machine
learning techniques is the availability of large databases that capture the
complex relations of interest. Previous datasets are focused on either 3D scene
representations with semantic information, tracking of multiple persons and
recognition of their actions, or activity recognition of a single person in
captured 3D environments. We present Bonn Activity Maps, a large-scale dataset
for human tracking, activity recognition and anticipation of multiple persons.
Our dataset comprises four different scenes that have been recorded by
time-synchronized cameras each only capturing the scene partially, the
reconstructed 3D models with semantic annotations, motion trajectories for
individual people including 3D human poses as well as human activity
annotations. We utilize the annotations to generate activity likelihoods on the
3D models called activity maps.
