  Modern object detectors take advantage of rectangular bounding boxes as a
conventional way to represent objects. When it comes to fisheye images,
rectangular boxes involve more background noise rather than semantic
information. Although multi-point representation has been proposed, both the
regression accuracy and convergence still perform inferior to the widely used
rectangular boxes. In order to further exploit the advantages of multi-point
representation for distorted images, Concentric Rectangles Regression
Strategy(CRRS) is proposed in this work. We adopt smoother mean loss to
allocate weights and discuss the effect of hyper-parameter to prediction
results. Moreover, an accurate pixel-level method is designed to obtain
irregular IoU for estimating detector performance. Compared with the previous
work for muti-point representation, the experiments show that CRRS can improve
the training performance both in accurate and stability. We also prove that
multi-task weighting strategy facilitates regression process in this design.
