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 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.