Classifying tracked objects in far-field video surveillance
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55693398-MIT.pdf
Description
Full printable version
Size
4.09 MB
Format
Adobe PDF
Checksum (MD5)
eb83697cc0648d037b040c6f142ea369
Author(s)
Bose, Biswajit, 1981-
Advisor(s)
W. Eric L. Grimson.
Date Issued
2004
Publisher
Massachusetts Institute of Technology
Abstract
Automated visual perception of the real world by computers requires classification of observed physical objects into semantically meaningful categories (such as 'car' or 'person'). We propose a partially-supervised learning framework for classification of moving objects-mostly vehicles and pedestrians-that are detected and tracked in a variety of far-field video sequences, captured by a static, uncalibrated camera. We introduce the use of scene-specific context features (such as image-position of objects) to improve classification performance in any given scene. At the same time, we design a scene-invariant object classifier, along with an algorithm to adapt this classifier to a new scene. Scene-specific context information is extracted through passive observation of unlabelled data. Experimental results are demonstrated in the context of outdoor visual surveillance of a wide variety of scenes.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.
Includes bibliographical references (p. 67-70).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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