  Image-based sports analytics enable automatic retrieval of key events in a
game to speed up the analytics process for human experts. However, most
existing methods focus on structured television broadcast video datasets with a
straight and fixed camera having minimum variability in the capturing pose. In
this paper, we study the case of event detection in sports videos for
unstructured environments with arbitrary camera angles. The transition from
structured to unstructured video analysis produces multiple challenges that we
address in our paper. Specifically, we identify and solve two major problems:
unsupervised identification of players in an unstructured setting and
generalization of the trained models to pose variations due to arbitrary
shooting angles. For the first problem, we propose a temporal feature
aggregation algorithm using person re-identification features to obtain high
player retrieval precision by boosting a weak heuristic scoring method.
Additionally, we propose a data augmentation technique, based on multi-modal
image translation model, to reduce bias in the appearance of training samples.
Experimental evaluations show that our proposed method improves precision for
player retrieval from 0.78 to 0.86 for obliquely angled videos. Additionally,
we obtain an improvement in F1 score for rally detection in table tennis videos
from 0.79 in case of global frame-level features to 0.89 using our proposed
player-level features. Please see the supplementary video submission at
https://ibm.biz/BdzeZA.
