  Autonomous driving requires various computer vision algorithms, such as
object detection and tracking.Precisely-labeled datasets (i.e., objects are
fully contained in bounding boxes with only a few extra pixels) are preferred
for training such algorithms, so that the algorithms can detect exact locations
of the objects. However, it is very time-consuming and hence expensive to
generate precise labels for image sequences at scale. In this paper, we propose
DeepBbox, an algorithm that corrects loose object labels into right bounding
boxes to reduce human annotation efforts. We use Cityscapes dataset to show
annotation efficiency and accuracy improvement using DeepBbox. Experimental
results show that, with DeepBbox,we can increase the number of object edges
that are labeled automatically (within 1\% error) by 50% to reduce manual
annotation time.
