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Margin based likelihood map fusion for target tracking | IEEE Conference Publication | IEEE Xplore

Margin based likelihood map fusion for target tracking


Abstract:

Visual object recognition and tracking can be formulated as an object-background classification problem. Since combining multi-modal information is known to exponentially...Show More

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 confidence 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.
Date of Conference: 22-27 July 2012
Date Added to IEEE Xplore: 10 November 2012
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Conference Location: Munich, Germany

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