Ontology-Based Global and Collective Motion Patterns for Event Classification in Basketball Videos

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. By analyzing the influence of different stages of video segments to semantic events discrimination, it is observed that the pre-event and event-occ segments are effective for classification, while the post-events are effective for event success/failure classification. In this paper, we propose an ontology-based global and collective motion pattern (On_GCMP) algorithm for the basketball event classification. First, a two-stage GCMP-based event classification scheme is proposed. The GCMP is extracted using the 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 the sequential convolutional neural networks (CNNs) and the long short-term memory (LSTM) networks to extract the spatial and temporal features of GCMP for event classification. Second, we utilize the 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 the state of the art on NCAA.