Detecting pitching frames in baseball game video using Markov random walk

Pitching is the starting point of an event in baseball games. Hence, locating pitching shots is a critical step in content analysis of a baseball game video. However, pitching frames vary with innings and games. Existing methods that require a great deal of effort to construct empirical rules or label training data do not capture the characteristics of various pitching frames very well. In this paper, we present an unsupervised method for pitching frame detection by using Markov random walk. A video stream is first divided into content-homogeneous shots, and these shots are merged into states through hierarchical agglomerative clustering. Then, the state with the highest visit probability according to the Markov random walk theory is deemed the set of pitching frames. Finally, a model trained on the pitching frames in the pitching state is further used to detect the remaining potential pitching frames in other states. Our experiments demonstrate that the proposed method yields satisfactory results in a variety of MLB games.