Arthur Goshtasby (Committee Member), Juan Vasquez (Committee Member), Thomas Wischgoll (Advisor)
Master of Science in Computer Engineering (MSCE)
In the field of surveillance, algorithms are developed to extract meaningful information out of a video feed captured via a camera. One type of algorithm used in the field of surveillance is a tracking algorithm. A tracking algorithm allows a user to watch the movement of an object in the camera's field of view. The tracker used in this thesis research is a feature aided tracker (FAT). The FAT uses both features and kinematics to generate tracks. However, camera movement will affect the tracker's ability to accurately track an object which poses a problem to the tracker. Specifically, the camera will introduce the multi-fragmentation problem to the tracker.
Multi-fragmentation occurs when an object is marked with two tracks instead of a single track. By marking the object with two tracks, the tracker's performance and accuracy will decrease. This thesis research proposes the idea of matching features of small foreground objects (fragments) to create larger foreground objects. A pair of fragments will have their features calculated into a score. If the fragment pair's score is below a specific threshold, they will be matched to create a larger fragment. Many of the concepts used to design this tracking algorithm (FAME) stem from the fields of computer vision, pattern recognition, and tracking.
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
Copyright 2014, all rights reserved. This open access ETD is published by Wright State University and OhioLINK.