模板自适应的Mean Shift红外目标跟踪

为了解决Mean Shift跟踪算法中目标模板只能从单一图像建立且很难更新问题，提出了一种结合改进的Mean Shift与增量式支持向量机的红外目标跟踪算法。首先，根据目标区域的灰度直方图对目标进行描述，然后采用标准Mean Shift搜索目标，结合子图图像矩特征进行二次搜索，再计算下一帧搜索的窗口大小，以解决目标尺寸明显变化时造成目标丢失的问题。同时，针对目标遮挡易导致跟踪失败的问题，引入机器学习理论，采用增量式支持向量机自适应更新模板，则目标跟踪问题转换为目标和背景的分类问题。实验结果表明:提出的改进算法在目标尺寸、姿态发生变化或出现部分遮挡时，能有效跟踪目标。

Infrared target tracking based on template adaptive Mean Shift

In order to solve the problem that the target template of standard Mean Shift tracking can only be built from a single image, and difficult to update, an algorithm combining improved Mean Shift with incremental Support Vector Machine for infrared target tracking was proposed. First, target was described using gray histogram of the target region. Then, in order to solve the problem of target lost in tracking caused by target size obviously changing, target localization was started using standard Mean Shift, and then image moment feature of the sub image for secondary search was combined to calculate the tracking window size for next frame. Meanwhile, according to the problem of target occlusion easily lead to tracking failure, machine learning theory was introduced and incremental support vector machine was used to update target template adaptively, thus target tracking problem was converted to a problem of classification between the target and the background. Experiments show that the improved algorithm proposed in this paper performs well even if greatly change occurs in target pose, size or partial occlusion happens.