Ontology-Based Action Recognition in Sport Videos Using Semantic Verification Model

Sports employ computer vision for a variety of applications, including interactive viewing, object interpretation, sports action analysis, and intelligent rule-based systems for better referee choices. The computer vision problem known as “sports action recognition” looks for interactions between players and related sporting items. Most of the existing solutions in sports action recognition are complex and incomplete as taking both playfield and non-play field objects with various actions for the same human posture into account. Hence, the proposed model focuses only on objects within the playfield and the right functional action that is performed by the human. We identify the play field using a quadrant-based density (QD) approach which locates the crowded region and then morphological operations are applied to spot only play region. The right action performed is identified using the logic-based query to the Sports Action Generation model (SAG) which is generated using the newly constructed sports ontology. The proposed semantic verification model using ontology typically offers more transparency compared to deep learning networks. It relies on explicit rules and logic that can be easily interpreted, enhancing interoperability and accuracy across systems. We use the sports video wild (SVW) and UCF-101 datasets to assess the performance of our proposed model. The proposed sports action recognition system achieves action identification in videos with a good accuracy of 96%.