Dual-threshold Segmentation Method for Infrared Pedestrian
The gray level difference between the pedestrians and environment is small in infrared images, and it is easy to appear the problem of fault segmentation while segmenting. This paper proposes a dual-threshold segmentation method for infrared pedestrian. The global threshold of the image is computed by using the statistical variance, and it is used for a preliminary segmentation. A cross-shaped sliding window is introduced to scan the image. The local threshold of each pixel in the initial segmentation objective area is computed by using statistical variance. By means of the classified formula, the pixel can be classified into objects or the background area. The binary image is obtained. Experiments show that this method improves the segmentation accuracy, and has good performance on pedestrian segmentation.