Robust Background-Subtraction for Person Detection in Thermal Imagery

We present a new contour-based background-subtraction technique to detect people in widely varying thermal imagery. Statistical background-subtraction is first used to identify local regions-of-interest. Within each region, gradient information in the foreground and background are combined to form a contour saliency map. After thinning, an A* path-constrained search along watershed boundaries is used to complete any broken contour segments. Lastly, the contour image is flood-filled to produce silhouettes. Results are presented that demonstrate the robustness of the approach to detect people across a wide range of thermal imagery using a fixed set of parameters.