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High-level event detection in broadcast sports video

This thesis investigates semantic analysis of broadcast sports footage. A domain dependent sports video model is proposed. Under this model, the game semantics can be derived according to their relationship with the sequence of dynamic events that occur in the sport and the evolution of the spatio-temporal behaviour of a relevant object. Snooker and tennis are targeted as typical broadcast sports footage for the purpose of this research. The problem focus is to automatically extract semantically meaningful events and to convey a useful representation to the user. Access to semantics provides a more natural tool for a user to query a corpus of data than by low-level content based features alone. These semantics are however open to various interpretations by different viewers. Therefore, in order to create a successful semantic based retrieval system it is necessary to consider the user-context. Unconstrained sports footage is generally very complicated in structure, so restricting the domain being addressed enables a viewer model to be created. Domain specific features are extracted from the raw footage. These can then be exploited to develop algorithms which understand the characteristics of the data and the requirements of the user. These algorithms enable low-level domain features to be mapped to high-level semantics by learning the evolution of the features. Traditionally, low-level visual features have been used to summarise the content in view. Global colour, texture and motion have all been used for this purpose. In this thesis a novel algorithm is presented which captures the geometry of the scene without having to extract and reconstruct complicated 3D scene geometry. Hidden Markov models are then used in a novel fashion to model these observations for camera view classification. A new extension of the colour based Particle Filter is employed to track objects. It encourages better tracking in a constrained sports environment by exploiting prior scene geometry and playing surface colour information. The implementation of the tracker also allows for object collision and disappearance to be detected. The performance of the tracker is assessed using geometrical measures and by comparing it to the tracking produced using a gradient based motion estimator. Thus far, retrieval of semantic events from sports footage has relied on prior knowledge of the broadcast video syntax. Typically, the temporal interleaving of camera views has been used to infer these important events or highlights in the footage. In this research, the spatio-temporal behaviour of tennis players and snooker balls are considered as being the embodiment of a semantic event. This concept offers a new means of automatically extracting semantic episodes from sports footage. The scope of this thesis could easily be extended by further investigation into retrieval of semantic events from the vast quantities of other live and archived sports footage.