Reliable target detection and tracking within strong clutters in outdoor infrared video sequences present great challenges. This is caused by several artificial and natural conditions, such as luminance change resulted from automatic gain adjustment in the IR camera, and other extreme granularity and uncontrolled environmental factors. In some important applications, the targets of interest include vehicles in motion and people in transit. We propose a new integrated solution to address all these issues. The system is composed of the following components: a region masking algorithm to divide a video frame into reliable and unreliable regions, a novel motion pattern recognition module, and an automatic walking-person recognition module. Such a comprehensive technique makes it possible for real-world outdoor infrared surveillance applications, where environmental interferences are uncontrolled and target motion are complex. Extensive experiments were carried out on real-world outdoor infrared videos provided by General Dynamics. In all tested sequences, the proposed algorithm had successfully detected and tracked the targets consistently throughout the life cycle of the targets, even when some targets were seriously blurred or occluded. Our results show that the proposed algorithm is practical, robust, and reliable for low-quality outdoor infrared videos.