Margin based likelihood map fusion for target tracking

Jing Peng, Guna Seetharaman

Research output: Contribution to conferencePaperpeer-review

1 Scopus citations


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
Number of pages4
StatePublished - 2012
Event2012 32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012 - Munich, Germany
Duration: 22 Jul 201227 Jul 2012


Other2012 32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012


  • Classification
  • Fusion
  • Large margin


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