Abstract: In intelligent video surveillance field，the moving target detection is one of the fundamental tasks and an active topic for image sequence analysis.When detecting big target moving slowly in nearby view，aiming at the shortcoming of the temporal detecting algorithm which can only detect edge and internal holes of complete segmentation problems，it proposes a method of close-range moving target detection based on the space-time information.This algorithm uses spatial information of image to research foreground/background division technology，based on the multimodal mean of motion history at temporal domain.Then，by minimizing energy model，the structure of network and the theory of network flow，it converts the target detection to the problem of max-flow/min-cut.Experiment shows that the proposed algorithm can overcome gradual changes of the illumination，background disturbances and slight shaking of the camera，and update foreground/background effectively in the multimodal environment.It can detect moving targets accurately and fully.

Key words: close-range moving target detection, multimodal mean, foreground/background segmentation, max-flow/min-cut

摘要： 运动目标检测是智能视频监控中图像序列分析的基础和研究热点，针对时域算法在检测近景大目标缓慢运动时，仅能检测出目标边缘、内部存在大量空洞等完整分割问题，提出了一种结合时空特征的近景运动目标检测算法。该算法在时域运动历史多模态均值背景模型的基础上，运用图像空域信息研究前/背景分割技术，通过能量最小化模型、网络构造及网络流理论，把目标检测转换成最大流/最小割问题。实验表明，该算法能在复杂环境中克服光照缓慢变化、背景扰动和摄像机轻微抖动，有效转换前/背景，准确完整地分割大运动目标。

关键词: 近景运动目标检测, 多模态均值, 前/背景分割, 最大流/最小割