# Dynamic Belief Fusion for Object Detection Source: [Dynamic Belief Fusion for Object Detection]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:13:30.351Z Authors: Ryan Robinson Published: 2015-02-26T17:31:15.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()