  We consider the problem of detecting objects, as they come into view, from
videos in an online fashion. We provide the first real-time solution that is
guaranteed to minimize the delay, i.e., the time between when the object comes
in view and the declared detection time, subject to acceptable levels of
detection accuracy. The method leverages modern CNN-based object detectors that
operate on a single frame, to aggregate detection results over frames to
provide reliable detection at a rate, specified by the user, in guaranteed
minimal delay. To do this, we formulate the problem as a Quickest Detection
problem, which provides the aforementioned guarantees. We derive our algorithms
from this theory. We show in experiments, that with an overhead of just 50 fps,
we can increase the number of correct detections and decrease the overall
computational cost compared to running a modern single-frame detector.
