A passive infrared or Pyroelectric InfraRed (PIR) sensor is mainly used to sense the existence of moving objects in an indoor environment. However, in an outdoor environment, there are often outbreaks of false alarms from environmental changes and other sources. Therefore, it is difficult to provide reliable detection outdoors. In this paper, two algorithms are proposed to reduce false alarms and provide trustworthy quality to surveillance systems. The authors gather PIR signals outdoors, analyze the collected data, and extract the target features defined as window energy and alarm duration. Using these features, they model target and false alarms, from which they propose two target decision algorithms: window energy detection and alarm duration detection.