  Training high-accuracy object detection models requires large and diverse
annotated datasets. However, creating these data-sets is time-consuming and
expensive since it relies on human annotators. We design, implement, and
evaluate TagMe, a new approach for automatic object annotation in videos that
uses GPS data. When the GPS trace of an object is available, TagMe matches the
object's motion from GPS trace and the pixels' motions in the video to find the
pixels belonging to the object in the video and creates the bounding box
annotations of the object. TagMe works using passive data collection and can
continuously generate new object annotations from outdoor video streams without
any human annotators. We evaluate TagMe on a dataset of 100 video clips. We
show TagMe can produce high-quality object annotations in a fully-automatic and
low-cost way. Compared with the traditional human-in-the-loop solution, TagMe
can produce the same amount of annotations at a much lower cost, e.g., up to
110x.
