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KI-Glossar

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Object Center

Central point of a detected object, serving as the main reference in CenterNet to infer the complete dimensions of the bounding box.

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Size Regression

Prediction branch in CenterNet that estimates the width and height of each object from its detected central point.

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Point-based Detection

Detection paradigm that replaces anchor boxes with direct prediction of keypoints, reducing complexity and improving localization accuracy.

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Center Offset

Offset vector predicted by CenterNet to adjust the position of an object's center from the discrete grid to its exact continuous location.

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Top-left Keypoint

Characteristic point detected in certain CenterNet variants corresponding to the top-left corner of the object's bounding box.

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Bottom-right Keypoint

Complementary characteristic point detected to define the bottom-right corner of the bounding box, enabling complete object reconstruction.

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Object Keypoint Similarity (OKS)

Evaluation metric that measures the precision of keypoint detection by taking into account the scale and variance of each detected keypoint.

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Convolutional Pose Machine

Sequential architecture that influences CenterNet in its ability to progressively refine keypoint predictions through multiple convolutional stages.

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Spatial Resolution Preservation

CenterNet's ability to maintain high spatial resolution in feature maps for accurate object center localization.

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