  This paper introduces a novel self-learning framework that automates the
label acquisition process for improving models for detecting players in
broadcast footage of sports games. Unlike most previous self-learning
approaches for improving appearance-based object detectors from videos, we
allow an unknown, unconstrained number of target objects in a more generalized
video sequence with non-static camera views. Our self-learning approach uses a
latent SVM learning algorithm and deformable part models to represent the shape
and colour information of players, constraining their motions, and learns the
colour of the playing field by a gentle Adaboost algorithm. We combine those
image cues and discover additional labels automatically from unlabelled data.
In our experiments, our approach exploits both labelled and unlabelled data in
sparsely labelled videos of sports games, providing a mean performance
improvement of over 20% in the average precision for detecting sports players
and improved tracking, when videos contain very few labelled images.
