论文标题
DBF：组合多个对象检测器的动态信念融合
DBF: Dynamic Belief Fusion for Combining Multiple Object Detectors
论文作者
论文摘要
在本文中，我们提出了一种称为动态信念融合（DBF）的新颖且高度实用的得分级融合方法，该方法直接从多个对象检测方法中直接整合了单个检测的推理得分。为了有效地整合多个检测器的各个输出，使用基于相应检测器的Precision-Recall关系构建的置信度模型来估算每个检测分数中的歧义水平。对于每个检测器的输出，DBF然后根据基于单个检测器的先验置信度模型的检测得分的条件来计算三个假设（目标，非目标和中间状态（目标，目标或非目标））的概率，这被称为基本概率分配。所有检测器的三个假设上的概率分布都通过Dempster的组合规则最佳地融合。 ARL，Pascal VOC 07和12个数据集的实验表明，DBF的检测准确性显着高于任何基线融合方法，以及用于融合的单个检测器。
In this paper, we propose a novel and highly practical score-level fusion approach called dynamic belief fusion (DBF) that directly integrates inference scores of individual detections from multiple object detection methods. To effectively integrate the individual outputs of multiple detectors, the level of ambiguity in each detection score is estimated using a confidence model built on a precision-recall relationship of the corresponding detector. For each detector output, DBF then calculates the probabilities of three hypotheses (target, non-target, and intermediate state (target or non-target)) based on the confidence level of the detection score conditioned on the prior confidence model of individual detectors, which is referred to as basic probability assignment. The probability distributions over three hypotheses of all the detectors are optimally fused via the Dempster's combination rule. Experiments on the ARL, PASCAL VOC 07, and 12 datasets show that the detection accuracy of the DBF is significantly higher than any of the baseline fusion approaches as well as individual detectors used for the fusion.
