基于红外和可见光融合的目标跟踪

摘要

针对单一图像源下目标跟踪精度不高和当目标存在部分遮挡时目标跟踪丢失的问题,本文提出了一种结合红外图像和可见光图像特征进行融合的方法.首先在进行目标跟踪时,提取可见光图像的颜色信息作为目标模型的参数,提取红外图像的灰度信息作为目标模型的参数,并分别得到目标位置及其子图.然后再利用目标子图和目标模型分别进行Bhattacharyya系数的计算,根据权值函数来计算各自系数的权值,最后用Mean Shift算法对加权后的目标进行跟踪.该方法充分利用了红外图像与可见光图像的优点,提高了目标跟踪的精度,解决了当目标存在部分遮挡时目标跟踪丢失的问题.%In the process of target tracking, the accuracy of tracker for single image source is not high enough and the target would be inclined to be lost when covered partially. A method of fusing the features of infrared image and visible light image is proposed in this study. First, the color information of the visible light image is extracted as a parameter in the target model, and the gray level information of the infrared image is taken as the other parameter. According to the two parameters, the target positions and its subgraphs can be acquired respectively. Then the corresponding Bhattacharyya coefficients are calculated by the anterior target subgraphs and the target models. The weights of the respective coefficients can be calculated on the basis of the weighting function. Finally, the target that is weighted with the Mean Shift algorithm could be tracked. This method makes full use of the advantages of infrared images and visible light images, improves the accuracy of tracker, and has solved the problem that the target is likely to be lost when partially covered.