In this study, we proposed an improved method of infrared pedestrian detection and overlap rate, which could be used for positioning and abnormal alarm, especially in the security industry. The method is consisted of three steps: 1) infrared pedestrian detection algorithm; 2) classification algorithm; 3) overlap rate algorithm and the logic of alarm. Infrared sensors could collect high quality image data at night, and overcome environmental interference as much as possible. Pedestrian detection was designed by YOLOv3 algorithm and Multi-Layer Perception (MLP) based on Histogram of Oriented Gradient (HOG) features. The abnormal alarms were proposed by calculating overlap rate between pedestrian detection bound and ground truth bound, and then making logical judgment. The experiments evidenced the benefits of proposed approach, which could effectively improve pedestrian detection performance and abnormal alarm accuracy (over 91%).