  We propose a neural network architecture that learns body part appearances
for soccer player re-identification. Our model consists of a two-stream network
(one stream for appearance map extraction and the other for body part map
extraction) and a bilinear-pooling layer that generates and spatially pools the
body part map. Each local feature of the body part map is obtained by a
bilinear mapping of the corresponding local appearance and body part
descriptors. Our novel representation yields a robust image-matching feature
map, which results from combining the local similarities of the relevant body
parts with the weighted appearance similarity. Our model does not require any
part annotation on the SoccerNet-V3 re-identification dataset to train the
network. Instead, we use a sub-network of an existing pose estimation network
(OpenPose) to initialize the part substream and then train the entire network
to minimize the triplet loss. The appearance stream is pre-trained on the
ImageNet dataset, and the part stream is trained from scratch for the
SoccerNet-V3 dataset. We demonstrate the validity of our model by showing that
it outperforms state-of-the-art models such as OsNet and InceptionNet.
