Research at the Computer Vision Laboratory at the University of Maryland has focused on developing algorithms and systems that can look at humans and recognize their activities in near real-time. Our earlier implementation (theW4 system) while quite successful was restricted to applications with a fixed camera. In this paper, we present some recent work that removes this restriction. Such systems are required for machine vision from moving platforms such as robots, intelligent vehicles, and unattended large field of regard cameras with a small field of view. Our approach is based on the use of a deformable shape model for humans coupled with a novel variant of the Condensation algorithm that uses quasi-random sampling for efficiency. This allows the use of simple motion models, which results in algorithm robustness, enabling us to handle unknown camera/human motion with unrestricted camera viewing angles. We present the details of our human tracking algorithms and some examples from pedestrian tracking and automated surveillance.