Target Fusion Detection Method Based on Spatiotemporal Multimodal Mean Model
基于多模式均值时空模型的目标融合检测方法

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.