  Automatic analysis of the video is one of most complex problems in the fields
of computer vision and machine learning. A significant part of this research
deals with (human) activity recognition (HAR) since humans, and the activities
that they perform, generate most of the video semantics. Video-based HAR has
applications in various domains, but one of the most important and challenging
is HAR in sports videos. Some of the major issues include high inter- and
intra-class variations, large class imbalance, the presence of both group
actions and single player actions, and recognizing simultaneous actions, i.e.,
the multi-label learning problem. Keeping in mind these challenges and the
recent success of CNNs in solving various computer vision problems, in this
work, we implement a 3D CNN based multi-label deep HAR system for multi-label
class-imbalanced action recognition in hockey videos. We test our system for
two different scenarios: an ensemble of $k$ binary networks vs. a single
$k$-output network, on a publicly available dataset. We also compare our
results with the system that was originally designed for the chosen dataset.
Experimental results show that the proposed approach performs better than the
existing solution.
