Automatic tracking of moving objects in video for surveillance applications
Issue Date
2007-08-31Author
Narayana, Manjunath
Publisher
University of Kansas
Type
Thesis
Degree Level
M.S.
Discipline
Electrical Engineering & Computer Science
Rights
This item is protected by copyright and unless otherwise specified the copyright of this thesis/dissertation is held by the author.
Metadata
Show full item recordAbstract
Automated surveillance systems are of critical importance for the field of security. The task of reliably detecting and tracking moving objects in surveillance video, which forms a basis for higher level intelligence applications, has many open questions. Our work focuses on developing a framework to detect moving objects and generate reliable tracks from real-world surveillance video. After setting up a basic system that can serve as a platform for further automatic tracking research, we tackle the question of variation in distances between the camera and the objects in different parts of the scene (object depth) in surveillance videos. A feedback-based solution to automatically learn the distance variation in static-camera video scenes is implemented, based on object motion in different parts of the scene. The solution, based on a concept that we call 'Vicinity Factor', is robust to noise and object segmentation problems. The Vicinity Factor can also be applied to estimate spatial thresholds and object size in different parts of the scene. Further, a new Bayesian algorithm to assign tracks to objects in the video is developed. The Bayesian method allows for probabilistic track assignment and can be the basis for future higher-level inference.
Description
Thesis (M.S.)--University of Kansas, Electrical Engineering & Computer Science, 2007.
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