水平集活动轮廓模型是一种优秀的图像分割方法.针对红外人体检测系统中的图像分割难题，提出了一种基于水平集活动轮廓模型的新算法.该算法包含水平集运动检测模块、水平集亮度检测模块和融合模块.水平集运动检测模块融合了水平集和背景相减技术，通过演化水平集函数同时实现前景分割和背景估计，它用于检测序列中的运动区域，并将其演化结果输入到下一检测模块.水平集亮度检测模块融合了水平集和阈值分割技术.在给出双阈值时，可分割出亮度在双阈值所限定范围内图像区域，它用于检测序列图像序列中可能包含人体目标的全部区域.利用形态学开重建技术，融合模块在融合前两个模块检测结果后输出算法最终分割结果.此外，采用快速数值算法演化水平集检测模块以及优化设置整个算法流程，改善算法运行效率.实验结果表明，相对其他典型算法，该算法具有较高分割精度和运行效率，且对时序亮度变化和镜头运动鲁棒性更好.

The level set based active contour model (LSAC) has been proved advantageous for image segmentation. Based on LSAC techniques, a novel algorithm was proposed to overcome the difficulties of image segmentation in infrared human detection systems. It consists of a motion-based LSAC module, a threshold-based LSAC module and a fusion module. The motion-based LSAC, which bridges level set and background-subtraction techniques, conducts foreground segmentation and background estimation simultaneously based on converged level set functions. It works for detecting the moving regions in a sequence. Moreover, its output is regarded as the input of the threshold-based LSAC, which combines level set and thresholding techniques. This threshold-based LSAC module has the ability to extract the image regions having intensities within the range specified by dual thresholds and works for detecting all possible regions that may contain human candidates. Finally, the third module fuses the LSAC outputs and results in faithful segmentation result owing to the morphological open reconstruction. Furthermore, the fast numeric scheme proposed for evolving the LSAC modules and the optimized algorithmic flow improves efficiency. Experimental results demonstrate that the algorithm enjoys better performance in accuracy, efficiency and robustness to camera movement and temporal changes in the scene in comparison with the rival algorithms.