One of the main goals of artificial intelligence is to allow computers to understand the world around them. As humans we extract a large amount of knowledge about the world from our visual perception, and the field of computer vision is determined to give computers access to this same wealth of knowledge. One of the fundamental steps in understanding the world is finding specific objects within our field of view, and the related task of following these objects as they move.

In this thesis the Implicit Shape Model algorithm, a local feature-based object detection algorithm, is implemented and used to develop an appearance model and object tracking algorithm based on it. This algorithm is very robust to intraclass variation, and can successfully track objects when both occlusion and non-stationary backgrounds are present. The usefulness of the proposed appearance model is analyzed, and results of the algorithm on real video sequences are presented. Several enhancements to the method are also proposed, and performance in terms of recall and precision is analyzed.

Library of Congress Subject Headings

Computer vision; Optical pattern recognition; Pattern recognition systems; Automatic tracking; Image processing; Detectors

Publication Date


Document Type


Student Type


Degree Name

Computer Science (MS)

Department, Program, or Center

Computer Science (GCCIS)


Roger Gaborski

Advisor/Committee Member

Roxanne Canosa

Advisor/Committee Member

Carl Reynolds


Physical copy available from RIT's Wallace Library at TA1634 .C54 2005


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