Temporal post-processing of decision tree outputs for sports video categorisation

Abstract

In this paper, we describe a multistage decision making system to deal with the problem of automatic sports video classification. The system is founded on the concept of cues, i.e. pieces of visual evidence, characteristic of certain categories of sports that are extracted from key frames. The main decision making mechanism is a decision tree which generates hypotheses concerning the semantics of the sports video content. The final stage of the decision making process is a Hidden Markov Model system which bridges the gap between the semantic content categorisation defined by the user and the actual visual content categories. The latter is often ambiguous, as the same visual content may be attributed to different sport categories, depending on the context. We tested the system using two setups of HMMs. In the first, we construct and train an HMM model for each sport. A post-processing step is needed in this setup to combine the outcomes of the individual HMMs. In the second setup, we eliminate the need for post-processing by constructing a single HMM with each node representing one of the sports we want to detect. Comparing the results obtained from both setups showed that a single HMM delivered the better performance. © Springer-Verlag 2004.