  Many semantic events in team sport activities e.g. basketball often involve
both group activities and the outcome (score or not). Motion patterns can be an
effective means to identify different activities. Global and local motions have
their respective emphasis on different activities, which are difficult to
capture from the optical flow due to the mixture of global and local motions.
Hence it calls for a more effective way to separate the global and local
motions. When it comes to the specific case for basketball game analysis, the
successful score for each round can be reliably detected by the appearance
variation around the basket. Based on the observations, we propose a scheme to
fuse global and local motion patterns (MPs) and key visual information (KVI)
for semantic event recognition in basketball videos. Firstly, an algorithm is
proposed to estimate the global motions from the mixed motions based on the
intrinsic property of camera adjustments. And the local motions could be
obtained from the mixed and global motions. Secondly, a two-stream 3D CNN
framework is utilized for group activity recognition over the separated global
and local motion patterns. Thirdly, the basket is detected and its appearance
features are extracted through a CNN structure. The features are utilized to
predict the success or failure. Finally, the group activity recognition and
success/failure prediction results are integrated using the kronecker product
for event recognition. Experiments on NCAA dataset demonstrate that the
proposed method obtains state-of-the-art performance.
