Fusion-based background-subtraction using contour saliency

Abstract

We present a new contour-based background-subtraction technique using thermal and visible imagery for persistent object detection in urban settings. Statistical background-subtraction in the thermal domain is used to identify the initial regions-of-interest. Color and intensity information are used within these areas to obtain the corresponding regions-of-interest in the visible domain. Within each region, input and background gradient information are combined to form a Contour Saliency Map. The binary contour fragments, obtained from corresponding Contour Saliency Maps, are then combined. An A* path-constrained search along watershed boundaries is used to complete and close any broken contour segments. Lastly, the contour image is flood-filled to produce silhouettes. Results of our approach are presented and compared against manually segmented data.