  This paper proposes the first object counting method for omnidirectional
images. Because conventional object counting methods cannot handle the
distortion of omnidirectional images, we propose to process them using
stereographic projection, which enables conventional methods to obtain a good
approximation of the density function. However, the images obtained by
stereographic projection are still distorted. Hence, to manage this distortion,
we propose two methods. One is a new data augmentation method designed for the
stereographic projection of omnidirectional images. The other is a
distortion-adaptive Gaussian kernel that generates a density map ground truth
while taking into account the distortion of stereographic projection. Using the
counting of grape bunches as a case study, we constructed an original
grape-bunch image dataset consisting of omnidirectional images and conducted
experiments to evaluate the proposed method. The results show that the proposed
method performs better than a direct application of the conventional method,
improving mean absolute error by 14.7% and mean squared error by 10.5%.
