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This paper introduces a new mechanism called Feature Prominence to combine evidence from multiple feature operators for more reliable target detection and localization during video tracking. Feature prominence is measured using the statistical p-value estimated from a non-parametric local kernel density estimate of the a posteriori feature distribution. More prominent features have lower p-values and this ordering can be used to either discard low prominence features (high p-values) or reduce their weight during the feature fusion process to produce a more reliable fused feature likelihood map for locating the target at a subsequent time during tracking. The proposed feature fusion method is embedded within a test-bed tracking system. Detection and tracking performance of feature prominence as well as three other fusion methods are evaluated using the peak-recall and distance accuracy measures. Experimental results show that feature prominence outperforms these other feature fusion methods.