A novel approach for the fusion of detection scores from disparate object detection methods is proposed. In order to effectively integrate the outputs of multiple detectors, the level of ambiguity in each individual detection score (called "uncertainty") is estimated using the precision/recall relationship of the corresponding detector. The proposed fusion method, called Dynamic Belief Fusion (DBF), dynamically assigns basic probabilities to propositions (target, non-target, uncertain) based on confidence levels in the detection results of individual approaches. A joint basic probability assignment, containing information from all detectors, is determined using Dempster's combination rule, and is easily reduced to a single fused detection score. Experiments on ARL and PASCAL VOC 07 datasets demonstrate that the detection accuracy of DBF is considerably greater than conventional fusion approaches as well as state-of-the-art individual detectors.
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