#108: Feature prominence-based weighting scheme for video tracking

S. Candemir, K. Palaniappan, F. Bunyak, G. Seetharaman, and R. Rao

8th Indian Conference on Computer Vision, Graphics and Image Processing, 2012

wami, tracking, fmv, motion, features, fusion, dod

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This paper introduces a new mechanism called Feature Promi- nence 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 distri- bution. 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 fea- ture likelihood map for locating the target at a subsequent time during tracking. The proposed feature fusion method is embedded into a test tracking system. Then, detection and tracking performance of the system is evaluated. Ex- perimental results indicated that feature prominence out- performs several other feature fusion methods.