Date of Award


Document Type


Degree Name

Master of Science (MS)

Legacy Department

Electrical Engineering

Committee Chair/Advisor

Birchfield, Stanley

Committee Member

Hoover , Adam

Committee Member

Brooks , Richard


In this thesis, we present the design and implementation of a method for real-time person detection and tracking. Many current methods for detecting and tracking people rely on color contrast or movement to segment the image. Using color, however, requires the target and the background to be significantly different, and motion segmentation requires the target to be in constant motion relative to the background, often requiring stationary cameras. Pattern detection methods have also been applied to the problem of detecting pedestrians, but these approaches are slower and require stationary cameras to function. The method we present in this work does not require a color difference or constant motion to operate. We use Lucas-Kanade features to track feature points between left and right images, producing a sparse disparity map which is then segmented through the application of k-means clustering. We apply a Viola-Jones face detector to determine which, if any, of the resulting feature clusters represent a trackable person. This algorithm is tested using two identical standard cameras mounted on a mobile robot platform. Results are presented demonstrating detection and tracking of a person in several different situations, including partial occlusion and self-occlusion.



To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.