  Image and video compression has traditionally been tailored to human vision.
However, modern applications such as visual analytics and surveillance rely on
computers seeing and analyzing the images before (or instead of) humans. For
these applications, it is important to adjust compression to computer vision.
In this paper we present a bit allocation and rate control strategy that is
tailored to object detection. Using the initial convolutional layers of a
state-of-the-art object detector, we create an importance map that can guide
bit allocation to areas that are important for object detection. The proposed
method enables bit rate savings of 7% or more compared to default HEVC, at the
equivalent object detection rate.
