  In multi-person videos, especially team sport videos, a semantic event is
usually represented as a confrontation between two teams of players, which can
be represented as collective motion. In broadcast basketball videos, specific
camera motions are used to present specific events. Therefore, a semantic event
in broadcast basketball videos is closely related to both the global motion
(camera motion) and the collective motion. A semantic event in basketball
videos can be generally divided into three stages: pre-event, event occurrence
(event-occ), and post-event. In this paper, we propose an ontology-based global
and collective motion pattern (On_GCMP) algorithm for basketball event
classification. First, a two-stage GCMP based event classification scheme is
proposed. The GCMP is extracted using optical flow. The two-stage scheme
progressively combines a five-class event classification algorithm on
event-occs and a two-class event classification algorithm on pre-events. Both
algorithms utilize sequential convolutional neural networks (CNNs) and long
short-term memory (LSTM) networks to extract the spatial and temporal features
of GCMP for event classification. Second, we utilize post-event segments to
predict success/failure using deep features of images in the video frames
(RGB_DF_VF) based algorithms. Finally the event classification results and
success/failure classification results are integrated to obtain the final
results. To evaluate the proposed scheme, we collected a new dataset called
NCAA+, which is automatically obtained from the NCAA dataset by extending the
fixed length of video clips forward and backward of the corresponding semantic
events. The experimental results demonstrate that the proposed scheme achieves
the mean average precision of 58.10% on NCAA+. It is higher by 6.50% than
state-of-the-art on NCAA.
