Adaptive learning algorithm for SVM applied to feature tracking

Ashutosh Garg, Ira Cohen, Thomas S. Huang

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

Abstract

The framework of support vector machines (SVM) is becoming extremely popular in the field of statistical pattern classification. In this paper we investigate a technique which couples Kalman filter closely with the SVM. The problem of object tracking can be seen as a pattern recognition problem. However, because of the dynamics, this pattern might experience some changes over time. In order to keep track of the position of the pattern and to make out the desired pattern from the background, we must have some strong continuous time model. We propose an algorithm which combines the Markov property of the Kalman filter with the strong classification capability of SVM. The whole system has been tested on real life problems and we found that with this framework we could track a particular object even in a frame which contains identical objects. The results were compared to that of obtained by color blob tracking which showed the strength of the approach.

Original languageEnglish (US)
Title of host publicationProceedings - 1999 International Conference on Information Intelligence and Systems, ICIIS 1999
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages388-395
Number of pages8
ISBN (Electronic)0769504469, 9780769504469
DOIs
StatePublished - 1999
Event1999 International Conference on Information Intelligence and Systems, ICIIS 1999 - Bethesda, United States
Duration: Oct 31 1999Nov 3 1999

Publication series

NameProceedings - 1999 International Conference on Information Intelligence and Systems, ICIIS 1999

Other

Other1999 International Conference on Information Intelligence and Systems, ICIIS 1999
CountryUnited States
CityBethesda
Period10/31/9911/3/99

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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