The ability to automatically detect humans in infrared images has value in military and civilian
applications. Robots and unattended ground stations equipped with real-time human ATR capability can operate
as scouts, perform reconnaissance for military units, and serve to locate humans in remote or hazardous sites.
With the algorithm proposed in this study, human targets can be detected in infrared images based on the
structure and radiance of the human head. The algorithm works in a three step process. First, the infrared image
is segmented primarily based on edges and secondarily based on intensity of pixels. Once the regions of interest
have been determined, the segments undergo feature extraction, in which they are characterized based on
circularity and smoothness. The final step of the algorithm uses a k-Nearest Neighbor classifier to match the
segment's features to a database, determining whether the segment is a head or not. This algorithm operates best
in environments in which contrast between the human and the background is high, such as nighttime or indoors.
Tests show that 82% accuracy in identification of human heads is possible for a single still image. After
analyzing two uncorrelated frames viewing the same scene, the likelihood of correctly classifying a human head
that appears in both frames is 97%.