  This paper considers the task of detecting the ball from a single viewpoint
in the challenging but common case where the ball interacts frequently with
players while being poorly contrasted with respect to the background. We
propose a novel approach by formulating the problem as a segmentation task
solved by an efficient CNN architecture. To take advantage of the ball
dynamics, the network is fed with a pair of consecutive images. Our inference
model can run in real time without the delay induced by a temporal analysis. We
also show that test-time data augmentation allows for a significant increase
the detection accuracy. As an additional contribution, we publicly release the
dataset on which this work is based.
