Background subtraction based level sets for human segmentation in thermal infrared surveillance systems

Abstract Based on the technique of background subtraction, two level set based active contour models (LSACs) named as RT-BSLSAC and EA-BSLSAC are proposed for human segmentation in thermal infrared surveillance systems. The energy functional of RT-BSLSAC is initially formulated with the spatial–temporal information extracted from the background-subtracted images that correspond to the current frame and its adjacent frames. Then, minimization of such functional is conducted by a real-time numeric scheme evolving a binary level set function (BLSF). When the BLSF converges, the moving humans in current frame are detected with relatively complete interiors and enclosed, smooth contours. EA-BSLSAC makes two improvements to RT-BSLSAC. First, the formulation of energy functional not only depends on spatial–temporal information but also the boundary information resulting from an edge detector. Second, the functional is minimized by a convex numeric scheme featured by initialization-invariance. As a result, EA-BSLSAC presents higher segmentation accuracy but at more computational cost in comparison with RT-BSLSAC. Experimental results from segmenting the real-world infrared surveillance clips validate the advantages of the proposed methods in accuracy, efficiency, and the coordination with other algorithmic components of an infrared surveillance system due to the cancellation of post-processing meaning to reach complete human interiors and exact silhouettes.