Margin Based Likelihood Map Fusion for Target Tracking
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.
MSU Digital Commons Citation
Peng, Jing and Seetharaman, Guna, "Margin Based Likelihood Map Fusion for Target Tracking" (2012). Department of Computer Science Faculty Scholarship and Creative Works. 387.