Template Matching Between Visible Light and Infrared Images

In the field of computer vision, template matching technology is an important research direction. This technique compares the template image with the sample image to find out the position of the template image in the sample image. It has the characteristics of simple algorithm, small amount of calculation and high recognition rate, so it is usually used in other computer vision fields such as object detection and target tracking. In addition, with the popularity of infrared sensors, and infrared images can obtain additional information that is not included in visible light images, the integrated processing of visible light and infrared information has always been a research hotspot. The traditional template matching algorithm mainly focuses on the matching between visible light images. For the information difference between visible light and infrared images, the traditional template matching algorithms are difficult to achieve accurate matching between the two types of images, and the amount of calculation is large. In response to this problem, a template matching algorithm based on feature extraction of convolutional neural networks is proposed in this paper. Our method draws on the robust template matching using scale-adaptive deep convolutional features. We use a scale-adaptive method to extract the deep features of visible light and infrared images, and then uses the traditional NCC matching algorithm to obtain the matching position of the template on the feature map. Then the regression and optimization of the template position are performed to obtain the position of the template image on the sample image. The research results show that our method can achieve the matching of the infrared template on the visible light image, and the position error is not large.