Joint audio visual retrieval for tennis broadcasts

In recent years, there has been increasing work in the area of content retrieval for sports. The idea is generally to extract important events or create summaries to allow personalisation of the media stream. While previous work in sports analysis has employed either the audio or video stream to achieve some goal, there is little work that explores how much can be achieved by combining the two streams. This paper combines both audio and image features to identify the key episode in tennis broadcasts. The image feature is based on image moments and is able to capture the essence of scene geometry without recourse to 3D modelling. The audio feature uses PCA to identify the sound of the ball hitting the racket. The features are modelled as stochastic processes and the work combines the features using a likelihood approach. The results show that combining the features yields a much more robust system than using the features separately.