Classifying tracked objects in far-field video surveillance

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2004

Author dc:creator

• Bose, Biswajit, 1981-

Advisor dc:contributor.advisor

• W. Eric L. Grimson.

• Electrical Engineering and Computer Science.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/30100

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/30100