As an additional information source, the estimation of 3D motion fields is also developed. This leads to significantly improved generalisation, over standard spatiotemporal techniques. A large number of existing action recognition techniques are then implemented, and extensions formulated, to allow the encoding of 3D structural information. This data is obtained from 3D broadcast footage, which provides a broader range of variations, than may be feasibly produced, during staged capture in the lab. To evaluate the impact of this 3D information, and provide a benchmark to aid future development, a large multi-view action dataset is compiled, covering 14 different action classes and comprising over an hour of high definition video. #MOTION BACKGROUNG AND OVERLOOP TV#This is due to both the emergence of affordable 3D sensors, such as the Microsoft Kinect, and the ongoing growth of 3D broadcast footage (including 3D TV channels, and 3D Blu-Ray). The exploitation of these properties has become feasible in recent years. In addition, 3D information can remove projective distortions and the effect of camera orientation, and provides cues for occlusion. The issues of generalisation, may be mitigated to a significant extent, by utilising 3D information, which provides invariance to most appearance based variation. Algorithms which perform well under these circumstances, are generally well suited for real world deployment, in applications such as surveillance, video indexing, and assisted living. This leads to huge intra-class variability, including changes in lighting, actor appearance, viewpoint and action style. This ``in the wild'' recognition, favours algorithms with broad generalisation capabilities, as no constraints are placed on either the actor, or the setting. Particular emphasis is given to the task of natural action recognition. The aim of this thesis, is to develop estimation and encoding techniques for 3D information, which are applicable in a range of vision tasks.
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