Sports field recognition using deep multi-task learning

Abstract

In this paper we propose a novel approach to build a single shot regressor, called SFLNet, that directly predicts a parameter set relating a sports field seen in an input frame to its metric model. This problem is challenging due to the huge intra-class variance of sports fields and the large number of free parameters to be predicted. To address these issues, we propose to train our regressor in combination with semantic segmentation in a multi-task learning framework. We also introduce an additional module to exploit the spacial consistency of sports fields, which boosts both regression and segmentation performances. SFLNet can be trained with a dataset that can be semi-automatically built from human annotated point-to-point correspondences. To our knowledge, this work is the first attempt to solve this sports field localization problem relying only on an end-to-end deep learning framework. Experiments on our new dataset based on basketball games validate our approach over baseline methods.