由于基于亮度变化函数(IVF)的跟踪算法能高效跟踪前视红外图像中刚性目标但无法满足行人跟踪鲁棒性要求，提出了一种新的基于多热点亮度变化函数的红外图像中行人跟踪算法。分析了分区域、多热点描述行人目标热信号的必要性，利用改进的亮度变化函数在帧间目标窗口内定位热点，建立目标窗口自适应更新机制解决尺度变化问题，最后基于热点的运动特征描述子剔除定位于背景的野值点。对复杂红外场景的跟踪实验结果表明，由于在原始算法的基础上省去了模板匹配步骤及缩小了搜索对象的矩阵维数，该算法获得了最优的实时性; 且多热点机制使该算法的鲁棒性优于多种其他视觉跟踪算法，能够胜任存在遮挡、尺度变化、低对比度等干扰因素的前视红外图像中行人目标的跟踪。

As tracking algorithms based on Intensity Variation Function(IVF) can track effectively rigid targets in Forward Look Infrared(FLIR) imagery, but can not satisfy the need of a pedestrian tracking for robustness, a novel pedestrian tracking algorithm based on modified IVF was proposed. The necessity of describing the thermal signatures of pedestrians with multiple hot spots in divided subregions was analyzed. Then, the hot spots were detected in a target window from frame to frame by the modified IVF and an adaptive update mechanism for a target window was established to solve the scale change. Finally, the motion feature descriptors based on hot spots were used to remove the outliers detected unaccurately in the background. Comparative experiments on challenging thermal scenes demonstrate that the proposed algorithm outperforms the state-of-the-art approaches in real-time performance by removing the template matching step of original algorithm and decreasing the matrix dimension of searching objects. Moreover, by better robustness against many visual tracking algorithms with the multiple hot spot strategy, it is suitable for the pedestrian tracking in FLIR imagery with the interference of occlusion, scale changed and lower contrast.