NDLI: An unsupervised, online learning framework for moving object detection

Content Provider	IEEE Xplore Digital Library
Author	Nair, V. Clark, J.J.
Copyright Year	2004
Description	Author affiliation: Centre for Intelligent Machines, McGiIl Univ., Montreal, Que., Canada (Nair, V.; Clark, J.J.)
Abstract	Object detection with a learned classifier has been applied successfully to difficult tasks such as detecting faces and pedestrians. Systems using this approach usually learn the classifier offline with manually labeled training data. We present a framework that learns the classifier online with automatically labeled data for the specific case of detecting moving objects from video. Motion information is used to automatically label training examples collected directly from the live detection task video. An online learner based on the Winnow algorithm incrementally trains a task-specific classifier with these examples. Since learning occurs online and without manual help, it can continue in parallel with detection and adapt the classifier over time. The framework is demonstrated on a person detection task for an office corridor scene. In this task, we use background subtraction to automatically label training examples. After the initial manual effort of implementing the labeling method, the framework runs by itself on the scene video stream to gradually train an accurate detector.
Sponsorship	IEEE Comput. Soc
File Size	838542
File Format	PDF
ISBN	0769521584
ISSN	10636919
DOI	10.1109/CVPR.2004.1315181
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-06-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Object detection Labeling Face detection Layout Detectors Motion detection Streaming media Training data Machine learning algorithms Supervised learning
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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