Object localization refers to finding a bounding box for an object in a frame for the purpose of object detection.
I have read here on page 2 that Huber loss has a problem in object localization as it treats the bounding box coordinates as not correlated as, "Huber loss deals with each of the four parameters of the bounding box as independent and unrelated items however semantically that is not the case since the four coordinates of the bounding box are highly correlated and need to be treated as a single entity."
Huber loss is defined as follows, where $(y-\hat{y})$ is the L1 loss between the ground-truth $y$ and predicted bounding $\hat{y}$ and $\delta$ is a threshold equals to 0.5. $$ L_{\delta}= \left\{\begin{matrix} \frac{1}{2}(y - \hat{y})^{2} & if \left | (y - \hat{y}) \right | < \delta\\ \delta ((y - \hat{y}) - \frac1 2 \delta) & otherwise \end{matrix}\right. $$
Question: can you please help me understand the problem of Huber loss more related to object localization and why it deals with each of the four parameters of the bounding box as independent and unrelated items?
