  Activity recognition is very useful in scenarios where robots interact with,
monitor or assist humans. In the past years many types of activities -- single
actions, two persons interactions or ego-centric activities, to name a few --
have been analyzed. Whereas traditional methods treat such types of activities
separately, an autonomous robot should be able to detect and recognize multiple
types of activities to effectively fulfill its tasks. We propose a method that
is intrinsically able to detect and recognize activities of different types
that happen in sequence or concurrently. We present a new unified descriptor,
called Relation History Image (RHI), which can be extracted from all the
activity types we are interested in. We then formulate an optimization
procedure to detect and recognize activities of different types. We apply our
approach to a new dataset recorded from a robot-centric perspective and
systematically evaluate its quality compared to multiple baselines. Finally, we
show the efficacy of the RHI descriptor on publicly available datasets
performing extensive comparisons.
