Abstract

This paper 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. 1. Introduction Tracking human motion in an indoor environment is of interest in applications of surveillance. In particular, we are developing a methodology to track individuals at sites such as corridors, airports, borders, and secured buildings. This requires that the viewing system be able to image the tracked subject in a broad area over a long period of time. In pursuit of this goal, our work has evolved from studying human walking using a fixed camera [1, 2] to tracking non-background objects in a single moving camera [3]. The studies i...

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