  Detection identifies objects as axis-aligned boxes in an image. Most
successful object detectors enumerate a nearly exhaustive list of potential
object locations and classify each. This is wasteful, inefficient, and requires
additional post-processing. In this paper, we take a different approach. We
model an object as a single point --- the center point of its bounding box. Our
detector uses keypoint estimation to find center points and regresses to all
other object properties, such as size, 3D location, orientation, and even pose.
Our center point based approach, CenterNet, is end-to-end differentiable,
simpler, faster, and more accurate than corresponding bounding box based
detectors. CenterNet achieves the best speed-accuracy trade-off on the MS COCO
dataset, with 28.1% AP at 142 FPS, 37.4% AP at 52 FPS, and 45.1% AP with
multi-scale testing at 1.4 FPS. We use the same approach to estimate 3D
bounding box in the KITTI benchmark and human pose on the COCO keypoint
dataset. Our method performs competitively with sophisticated multi-stage
methods and runs in real-time.
