Abstract: Aiming at the problem that the incomplete segmentation occurred due to the intensity overlap between background and human targets in infrared human image,simplified pulse coupled neural networks(SPCNN) with adaptive multilevel threshold is proposed for infrared human image segmentation.The proposed method entirely abandons the mechanism of dynamic threshold decaying in time by the exponent term,and constructs multilevel threshold by using the region statistics of fired region and unfired region.Meanwhile,the clustering rule is introduced by combining the synchronous pulse mechanism to regulate the neurons firing in different object regions,so that it is possible to achieve high segmentation accuracy.Compared with several kinds of segmentation algorithms on real-world infrared images,experimental results show the higher efficiency and the lower misclassification of our method for human targets extracting.Furthermore,our model is superior over the traditional SPCNN in terms of parameters setting.