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Statistical Methods for the Visual Estimation of State

Intelligent beings learn and adapt to their environment. In Computer Vision, a system that learns is no longer reliant on hard-wired assumptions about the world and this report proposes an algorithm for efficient region tracking that borrows techniques from Machine Learning to build a flexible, adaptable system. The fully probabilistic ‘Relevance Vector Machine’ learns how visual inputs relate to motion, giving Gaussian observations that are fused into a robust state estimate. A support vector machine is used in tandem for automatic initialization and recovery. This approach operates in real-time using 50% of CPU time. The accuracy and robustness of temporal fusion are demonstrated and examples are given for tracking of faces, hands and cars in cluttered scenes.