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Fusion of thermal and visible images for dayight moving objects detection

机译：融合热图像和可见图像以检测昼/夜移动物体

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A background subtraction (BS) technique based on the fusion of thermal and visible imagery using Gaussian mixture models (GMM) is presented in this work. An automatic daytimeight-time detection is introduced that can be used to dynamically adapting the fusion scheme. Three fusion schemes are investigated and coined as early, late and image fusion. The first consists in augmenting the GMM model with thermal information prior to foreground segmentation. The second, as it name indicates, consists in the fusion of the outputs of BS applied to each sensor separately. The last one considers different linear combinations of both images forming a hybrid image. Most approaches improve the performance of the combined system by compensating the failures of individual sensors. Quantitative as well as qualitative results are shown to demonstrate the accuracy of each fusion approach with respect to foreground segmentation.
机译：在这项工作中提出了一种基于背景图像减法（BS）的技术，该技术基于使用高斯混合模型（GMM）的热图像和可见图像的融合。引入了自动白天/夜晚时间检测，可用于动态调整融合方案。研究了三种融合方案，并提出了早期，晚期和图像融合。首先是在前景分割之前用热信息扩充GMM模型。顾名思义，第二个是将分别应用于每个传感器的BS输出融合在一起。最后一个考虑了形成混合图像的两个图像的不同线性组合。大多数方法都是通过补偿单个传感器的故障来提高组合系统的性能。显示了定量和定性结果，以证明每种融合方法相对于前景分割的准确性。

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