  Infrared (IR) image segmentation is essential in many urban defence
applications, such as pedestrian surveillance, vehicle counting, security
monitoring, etc. Active contour model (ACM) is one of the most widely used
image segmentation tools at present, but the existing methods only utilize the
local or global single feature information of image to minimize the energy
function, which is easy to cause false segmentations in IR images. In this
paper, we propose a multi-feature driven active contour segmentation model to
handle IR images with intensity inhomogeneity. Firstly, an especially-designed
signed pressure force (SPF) function is constructed by combining the global
information calculated by global average gray information and the local
multi-feature information calculated by local entropy, local standard deviation
and gradient information. Then, we draw upon adaptive weight coefficient
calculated by local range to adjust the afore-mentioned global term and local
term. Next, the SPF function is substituted into the level set formulation
(LSF) for further evolution. Finally, the LSF converges after a finite number
of iterations, and the IR image segmentation result is obtained from the
corresponding convergence result. Experimental results demonstrate that the
presented method outperforms the state-of-the-art models in terms of precision
rate and overlapping rate in IR test images.
