To solve the problems of poor noise adaptability and blurred edge details of current pulse-coupled neural network (PCNN) methods in the application of human target infrared image segmentation under complex environments, an improved PCNN model is presented. Based on the characteristics of infrared noise, the weight matrix of the feeding input field is designed by the weighted mean value filtering and the anisotropic Gaussian filtering. The improved sum of modified Laplacian is introduced as the linking strength of the PCNN model to set this parameter adaptively. The dynamic threshold is expressed as the average gray value of the fired area to control PCNN iterative process. The proposed method is performed on more than 250 infrared human images from the IEEE OTCBVS database and the self-built database. The experimental results demonstrate that this method can effectively suppress infrared noise and keep many edge details of human targets. Compared with other PCNN segmentation models, the proposed method also shows good average probabilistic Rand index and low global consistency error.