CenterNet[1] is a point-based object detection framework, which can be easily extended to multiple computer vision tasks including object tracking, instance segmentation, human pose estimation, 3d object detection, action detection, human-object interaction detection, and many others.

Instead of classifying pre-defined anchors into objects and regressing corresponding bounding box shapes, CenterNet regards objects as points and directly regresses the center points of objects and the corresponding properties, e.g., the size of the bounding box, the offset, depth, or even the shape of the object. The properties of the object are highly customizable depending on the task and the problem to solve.

Ning Guanghan

Computer Vision Scientist @ JD Digits

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