Long sequence analysis of human motion using eigenvector decomposition

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Abstract
The work described in this paper takes place in the context of an attempt to develop a clearer understanding of what is meant by task oriented visual processing. The traditional separation of task and sensory processing within computer vision is something which has lead to an overemphasis on representation, modularity, and the single functionality of artificial vision systems. These issues are briefly discussed here in the context that the Karhunen Loeve Transform (KLT) is an algorithmic approach which allows us to begin to move away from a preconceived view of what vision is, how it works, and what it does. We summarize the mathematical background and basic operation of the KL coding procedure. We examine the application of the eigenvector decomposition to the analysis of human motion over several posture cycles of a walking sequence. The approach is used to examine the potential for recognition through motion, for posture description and for natural motion reconstruction processes. Various classification and analysis techniques, including artificial neural networks, the Fourier transform, and heuristic operations are used to extract the information available about the different cues investigated.

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