Category
Camera Calibration / Multi-view Camera
What
- A new deep learning framework for sports field registration using dense key points with an instance segmentation network.
- Introduces a new large-scale dataset for camera calibration in football.
Semantic segmentation treats several items in a category as one. Instance segmentation recognizes items into categories.
Why?
Why do we want to do this?
- Better camera calibration techniques.
- Previous datasets were small and mostly closed to the public.
Why was this not done before?
This paper is an improvement on,
A Robust and Efficient Framework for Sports-Field Registration
but uses an instance segmentation architecture instead of a semantic segmentation architecture.
How?
New Design Choices
Instance Segmentation / Dynamic Filter Learning
Dynamic Filter Learning Paper
I’m not convinced that dynamic filters are significant, but I don’t fully understand them.
Encoder-Decoder Architecture（U-Net）
Loss Functions
A weighted combination of the following.
- Binary Dice Loss
- Binary Cross Entropy
- Weighted Cross Entropy Loss
And?
Results are a little better/on par with previous state-of-the-art methods.
This note is a part of my paper notes series. You can find more here or on Twitter.