  A novel approach for the fusion of heterogeneous object detection methods is
proposed. In order to effectively integrate the outputs of multiple detectors,
the level of ambiguity in each individual detection score is estimated using
the precision/recall relationship of the corresponding detector. The main
contribution of the proposed work is a novel fusion method, called Dynamic
Belief Fusion (DBF), which dynamically assigns probabilities to hypotheses
(target, non-target, intermediate state (target or non-target)) based on
confidence levels in the detection results conditioned on the prior performance
of individual detectors. In DBF, a joint basic probability assignment,
optimally fusing information from all detectors, is determined by the
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 individual detectors used for the fusion.
