  In online action detection, the goal is to detect the start of an action in a
video stream as soon as it happens. For instance, if a child is chasing a ball,
an autonomous car should recognize what is going on and respond immediately.
This is a very challenging problem for four reasons. First, only partial
actions are observed. Second, there is a large variability in negative data.
Third, the start of the action is unknown, so it is unclear over what time
window the information should be integrated. Finally, in real world data, large
within-class variability exists. This problem has been addressed before, but
only to some extent. Our contributions to online action detection are
threefold. First, we introduce a realistic dataset composed of 27 episodes from
6 popular TV series. The dataset spans over 16 hours of footage annotated with
30 action classes, totaling 6,231 action instances. Second, we analyze and
compare various baseline methods, showing this is a challenging problem for
which none of the methods provides a good solution. Third, we analyze the
change in performance when there is a variation in viewpoint, occlusion,
truncation, etc. We introduce an evaluation protocol for fair comparison. The
dataset, the baselines and the models will all be made publicly available to
encourage (much needed) further research on online action detection on
realistic data.
