Objects as points

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Objects as Points
by Xingyi Zhou, D equan Wang and
Philipp Kr ähenbühl
Jurakuziev Dadajon
R eal - time Objec t detec tion
Objectsaspoints

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02
1. Computer Vision Tasks
2. Applications of Object Detection
3. Why Real-Time Object Detection?
4. Introduction to Objects as Points
5. Understanding the Architecture of Objects as Points
6. Performance
7. Conclusion
Agenda
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Computer Vision Tasks
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Applications of Object Detection
01 Self-driving Cars Face Recognition
03 Action Recognition 04 Object Counting
02
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Why Real-time Object Detection?
The model should be able to detect objects and make inferences within
microseconds.
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Objects as Points or CenterNet
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Objects as Points
To model an object as a single point — the center point of its bounding box.
The detector uses keypoint estimation to find center points and regresses to all other object
properties, such as size, 3D location, orientation, and even pose.
The center point based approach, CenterNet, is end-to-end differentiable, simpler, faster, and
more accurate than corresponding bounding box based detectors.
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Quick Explanation
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Bounding Box Keypoints

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Quick Explanation
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Bounding box based detectors : YOLO, RCNN

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Significance of Objects as Points
Objectsaspoints
• CenterNet's "anchor" only appears at the current
object's position instead of the entire picture, so
there is no such thing as a box overlap greater than
a positive anchor, and there is no need to
distinguish whether the anchor is an object or a
background.
• Each object has only one anchor, this anchor is
extracted from the keypoint estimation, so no
NMS is needed to filter.

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What is Keypoint Estimation?
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Architecture of CenterNet
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Objectsaspoints
From points to Bounding Boxes
- Extracts the peaks from each category from the heatmap
- Stores all intermediate values that are greater than or equal to
the eight surrounding pixels in the heatmap
- Leave behind 100 biggest peak values

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From points to Bounding Boxes
- The locations of the extracted peaks are expressed in integer
form (x, y).
- This allows to be shown the coordinates of the bounding box as
below

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Objects as Points
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All of these outputs come from the Single Keypoint Estimations.

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Performance
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Conclusion
Objectsaspoints
- A new representation for objects: as points
- This detector builds on successful keypoint estimation networks
- Finds object centers, and regresses to the size
- The algorithms is simple, fast, accurate
- End-to-end differentiable without any NMS post-processing

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COCO Dataset
- The COCO train, validation, and test sets, containing more than 200,000 images and 80 object
categories
- All object instances are annotated with a detailed segmentation mask. Annotations on the training
and validation sets (with over 500,000 object instances segmented) are publicly available.

Objects as points

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