Classifying tracked objects in far-field video surveillance
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2006
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| Online Access: | http://hdl.handle.net/1721.1/30100 |
| _version_ | 1811092127150505984 |
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| author | Bose, Biswajit, 1981- |
| author2 | W. Eric L. Grimson. |
| author_facet | W. Eric L. Grimson. Bose, Biswajit, 1981- |
| author_sort | Bose, Biswajit, 1981- |
| collection | MIT |
| description | Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004. |
| first_indexed | 2024-09-23T15:13:23Z |
| format | Thesis |
| id | mit-1721.1/30100 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T15:13:23Z |
| publishDate | 2006 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/301002019-04-11T14:36:29Z Classifying tracked objects in far-field video surveillance Bose, Biswajit, 1981- W. Eric L. Grimson. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004. Includes bibliographical references (p. 67-70). 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. by Biswajit Bose. S.M. 2006-03-24T18:19:31Z 2006-03-24T18:19:31Z 2004 2004 Thesis http://hdl.handle.net/1721.1/30100 55693398 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 70 p. 2893857 bytes 2893665 bytes application/pdf application/pdf application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Bose, Biswajit, 1981- Classifying tracked objects in far-field video surveillance |
| title | Classifying tracked objects in far-field video surveillance |
| title_full | Classifying tracked objects in far-field video surveillance |
| title_fullStr | Classifying tracked objects in far-field video surveillance |
| title_full_unstemmed | Classifying tracked objects in far-field video surveillance |
| title_short | Classifying tracked objects in far-field video surveillance |
| title_sort | classifying tracked objects in far field video surveillance |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/30100 |
| work_keys_str_mv | AT bosebiswajit1981 classifyingtrackedobjectsinfarfieldvideosurveillance |