A Novel Markov Logic Rule Induction Strategy for Characterizing Sports Video Footage

The grounding of high-level semantic concepts is a key requirement of video annotation systems. Rule induction can thus constitute an invaluable intermediate step in characterizing protocol-governed domains, such as broadcast sports footage. The authors propose a clause grammar template approach to the problem of rule induction in video footage of court games that employs a second-order meta-grammar for Markov Logic Network construction. The aim is to build an adaptive system for sports video annotation capable, in principle, of both learning ab initio and adaptively transferring learning between distinct rule domains. The authors tested the method using a simulated game predicate generator as well as real data derived from tennis footage via computer-vision-based approaches including HOG3D-based player-action classification, Hough-transform-based court detection, and graph-theoretic ball tracking. Experiments demonstrate that the method exhibits both error resilience and learning transfer in the court domain context. Moreover, the clause template approach naturally generalizes to any suitably constrained, protocol-governed video domain characterized by feature noise or detector error.