基于多模式均值时空模型的目标融合检测方法

针对复杂环境下的目标检测问题，提出了一种基于背景模型的融合检测方法。首先在多模式均值模型的基础上，构造多模式均值时空模型，结合像素在时空域上的分布信息，改善了模型对非平稳场景较为敏感的缺点，给出了模型更新方法和前景检测方法；然后利用该模型对可见光和红外图像序列分别进行建模和前景检测，给出了一种基于置信度的目标融合检测方法，利用双传感器信息提高检测精度和可靠性。实验结果验证了本文方法的有效性。

Target Fusion Detection Method Based on Spatiotemporal Multimodal Mean Model

Target detection is difficult to be realized in complex scenes when there are moving background objects such as trees. In this paper, a new target fusion detection method is proposed based on background model. Firsty, by combining temporal information of per-pixel and the spatial information in the local region, we introduce a variant of multimodal mean model called the spatiotemporal multimodal mean model that is well suited for the non-stationary scenes. Then, the proposed background model is separately used to extract foreground pixels in visible and infrared image sequences, and a fusion detection method based on the confidence map is proposed to get the target detection result. The multi-sensor information can improve the detection precision and handle different environmental conditions. Experiment results demonstrate the effectiveness of the proposed method.