Objects as Points
Objects as PointsXingyi ZhouUT WangUC Kr ahenb uhlUT identifies Objects as axis-aligned boxes in animage. Most successful object detectors enumerate a nearlyexhaustive list of potential object locations and classifyeach. This is wasteful, inefficient, and requires additionalpost-processing. In this paper, we take a different model an object as a single point the center pointof its bounding box. Our detector uses keypoint estima-tion to find center Points and regresses to all other ob-ject properties, such as size, 3D location, orientation, andeven pose. Our center point based approach, CenterNet, isend-to-end differentiable, simpler, faster, and more accuratethan corresponding bounding box based detectors. Center-Net achieves the best speed-accuracy trade-off on the MSCOCO dataset, at 142 FPS, at 52FPS, with multi-scale testing at FPS.
tors represent each object through an axis-aligned bounding box that tightly encompasses the object [18,19,33,43,46]. They then reduce object detection to image classification of an extensive number of potential object bounding boxes. For each bounding box, the classifier determines if the image content is a specific object or background. One-
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