Tracking human motion using multiple cameras

Presents a framework for tracking human motion in an indoor environment from sequences of monocular grayscale images obtained from multiple fixed cameras. Multivariate Gaussian models are applied to find the most likely matches of human subjects between consecutive frames taken by cameras mounted in various locations. Experimental results from real data show the robustness of the algorithm and its potential for real time applications.

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