Margin based likelihood map fusion for target tracking

Jing Peng, Guna Seetharaman

Research output: Contribution to conferencePaper

1 Scopus citations

Abstract

Visual object recognition and tracking can be formulated as an object-background classification problem. Since combining multi-modal information is known to exponentially quicken classification, often different features are used to create a set of representations for a pixel or target object. Each of the representations generates a probability of that pixel being part of the target object or scene background. Thus, how to combine these views to effectively exploit multi-modal information for classification becomes a key issue. We propose a margin based fusion technique for exploiting these heterogeneous features for classification, thus tracking. All representations contribute to classification on their learned con# dence scores (weights). As a result of optimally combining multi-modal information or evidence, discriminant object and background information is preserved, while ambiguous information is discarded. We provide experimental results that show its performance against competing techniques.

Original languageEnglish
Pages2292-2295
Number of pages4
DOIs
StatePublished - 1 Dec 2012
Event2012 32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012 - Munich, Germany
Duration: 22 Jul 201227 Jul 2012

Other

Other2012 32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012
CountryGermany
CityMunich
Period22/07/1227/07/12

Keywords

  • Classification
  • Fusion
  • Large margin

Fingerprint Dive into the research topics of 'Margin based likelihood map fusion for target tracking'. Together they form a unique fingerprint.

  • Cite this

    Peng, J., & Seetharaman, G. (2012). Margin based likelihood map fusion for target tracking. 2292-2295. Paper presented at 2012 32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012, Munich, Germany. https://doi.org/10.1109/IGARSS.2012.6351037