Learning of moving cast shadows for dynamic environments

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Scopus citations

Abstract

We propose a novel online framework for detecting moving shadows in video sequences using statistical learning techniques. In this framework, Support Vector Machines are applied to obtain a classifier that can differentiate between moving shadows and other foreground objects. The co-training algorithm of Blum and Mitchell is then used in an online setting to improve accuracy with the help of unlabeled data. We evaluate the concept of co-training and show its viability even when explicit assumptions made by the algorithm are not satisfied. Thus, given a small random set of labeled examples (in our application domain, shadow and foreground), the system gives encouraging generalization performance using a semi-supervised approach. In dynamic environments such as those induced by robot motion, the view changes significantly and traditional algorithms do not work well. Our method can handle such changing conditions by adapting online using a semi-supervised approach.

Original languageEnglish (US)
Title of host publication2008 IEEE International Conference on Robotics and Automation, ICRA 2008
Pages987-992
Number of pages6
DOIs
StatePublished - Sep 18 2008
Event2008 IEEE International Conference on Robotics and Automation, ICRA 2008 - Pasadena, CA, United States
Duration: May 19 2008May 23 2008

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Other

Other2008 IEEE International Conference on Robotics and Automation, ICRA 2008
CountryUnited States
CityPasadena, CA
Period5/19/085/23/08

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