  Modern deep convolutional neural networks (CNNs) for image classification and
object detection are often trained offline on large static datasets. Some
applications, however, will require training in real-time on live video streams
with a human-in-the-loop. We refer to this class of problem as time-ordered
online training (ToOT). These problems will require a consideration of not only
the quantity of incoming training data, but the human effort required to
annotate and use it. We demonstrate and evaluate a system tailored to training
an object detector on a live video stream with minimal input from a human
operator. We show that we can obtain bounding box annotation from
weakly-supervised single-point clicks through interactive segmentation.
Furthermore, by exploiting the time-ordered nature of the video stream through
object tracking, we can increase the average training benefit of human
interactions by 3-4 times.
