Tracking pedestrians through a network of cameras is a difficult and nuanced problem that is of significant interest in visual surveillance. This paper describes an online system for tracking multiple people as they move around a small camera network with partially-overlapping fields of view. Multi-object tracking is performed on each camera feed resulting in single-camera pedestrian tracks. Multi-camera tracking is then framed as an incremental data assignment of single-camera tracks to multi-camera tracks. Tracks are matched based on their visual similarity alone, a challenging task due to the high variability of a subject's appearance in different cameras. Results are presented on a ground truth data set.