In this thesis we present an operational computer vision system for real-time detection and tracking of human motion. The system captures monocular video of a scene and identifies those moving objects which are characteristically human. This serves as both a proof-ofconcept and a verification of other existing algorithms for human motion detection. An approach to statistical modeling of motion developed by Y. Song is coupled with a preprocessing stage of image segmentation and point feature tracking. This design allows a system that is robust with respect to occlusion, clutter, and extraneous motion. The results of experiments with the system indicate the ability to minimize both false detections and missed detections.