Year: 2004
Type: dissertation
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.
Author: B. Bose
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1580602297