Adaptive discriminative generativemodel and its applications

Ruei Sung Lin, David Ross, Jongwoo Lim, Ming Hsuan Yang

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

20 Citations (Scopus)


This paper presents an adaptive discriminative generativemodel that generalizes the conventional Fisher Linear Discriminant algorithm 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 from the background. We present a computationally efficient algorithm to constantly update this discriminativemodel as time progresses. While most tracking algorithms operate on the premise that the object appearance or ambient lighting condition does not significantly change as time progresses, our method adapts a discriminative generative model to reflect appearance variation of the target and background, thereby facilitating the tracking task in ever-changing environments. 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 publicationAdvances in Neural Information Processing Systems 17 - Proceedings of the 2004 Conference, NIPS 2004
PublisherNeural information processing systems foundation
ISBN (Print)0262195348, 9780262195348
Publication statusPublished - 2005 Jan 1
Event18th Annual Conference on Neural Information Processing Systems, NIPS 2004 - Vancouver, BC, Canada
Duration: 2004 Dec 132004 Dec 16

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258


Conference18th Annual Conference on Neural Information Processing Systems, NIPS 2004
CityVancouver, BC

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing


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