Adaptive discriminative generative model for object tracking

Ruei Sung Lin, Ming Hsuan Yang, Stephen E. Levinson

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


This paper presents an adaptive visual learning algorithm for object tracking. We formulate a novel discriminative generative framework that generalizes the conventional Fisher Linear Discriminant algorithm with a generative model and renders a proper probabilistic interpretation. Within the context of object tracking, we aim to find a discriminative generative model that best separates the target class from the background. We present a computationally efficient algorithm to constantly update this discriminative model as time progresses. While most tracking algorithms operate on the premise that the object appearance or environment lighting condition does not significantly change as time progresses, our method adapts the discriminative generative model to reflect appearance variation of the target and background, thereby facilitating the tracking task in different situations. Numerous experiments show that our method is able to learn a discriminative generative model for tracking target objects undergoing large pose and lighting changes.

Original languageEnglish
Title of host publicationECAI 2004 - 16th European Conference on Artificial Intelligence, including Prestigious Applications of Intelligent Systems, PAIS 2004 - Proceedings
EditorsRamon Lopez de Mantaras, Lorenza Saitta
PublisherIOS Press BV
Number of pages5
ISBN (Electronic)9781586034528
Publication statusPublished - 2004
Event16th European Conference on Artificial Intelligence, ECAI 2004 - Valencia, Spain
Duration: 2004 Aug 222004 Aug 27

Publication series

NameFrontiers in Artificial Intelligence and Applications
ISSN (Print)0922-6389
ISSN (Electronic)1879-8314


Conference16th European Conference on Artificial Intelligence, ECAI 2004

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence


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