针对前视红外时间序列图像，提出一种运动目标自动检测方法。利用三维(3D)时空纹理向量的变化度量运动特征，在视觉注意框架下，给出了运动目标的显著性定义。采用光流累加法校验并提取显著性运动区域作为视觉注意区域，将注意区域边界视作测地线主动轮廓的吸引力驱动可变形轮廓收敛于显著性运动目标，并利用目标梯度边缘作为收敛条件。实验结果表明，所提方法是合理、有效的。与传统运动检测方法相比，所提方法能够辨别冗余运动信息，对速率较快和较慢的运动对象以及噪声具有良好的稳健性。利用基于水平集的曲线演化计算能够同时给出多目标的边界定位，可为后续的识别跟踪以及3D重建与表示提供可靠依据。

In order to detect multi-motion targets in time sequenced forward looking infared (FLIR) images, a novel automatic detection method is proposed. First, the speed of 3D spatiotemporal texture vectors is used to measure motion features. Then, the saliency of motion target is defined under the frame of visual attention. So salient moving objects are detected and extracted by accumulating directionally-consistent optical flow. The regions are regarded as final visual attention regions. An improved geodesic active contour model is built, where the topology-free deformable evolving curve is attracted by the boundaries of attention regions. A gradient based convergence term forces the curves evolution to stop on the object boundaries. The validity of the proposed method is proved experimentally and the results of multi-dataset are given. Compared to other traditional methods, the proposed method has a good noise tolerance and can handle fast or slow image variations. It has no response to unnecessary motion and only focuses on the salient motion targets. The multiple targets boundaries are located due to implementation of level set scheme, which gives a reliable input for targets identifying and tracking or 3D reconstruction and representation.