An occluded human segmentation method in infrared image was presented in this paper. Firstly

the occluded regions binary mask was obtained using traditional infrared image segmentation methods such as threshold selection or histogram clustering. Then

two mean shift clustering process were carried out separately using the objects relative row and column coordinates to the mask as feature sets. Each pixels convergence position in vertical and horizontal direction was represented by a complex vector. Finally

another mean shift process was carried out on all the complex vectors

and the object masks pixels were classified according to their corresponding complex vectors cluster. Thus

the objects were segmented in the binary mask. Compared with the watershed algorithm

the segmented results of our algorithm were complete and could be controlled from coarse to fine by changing the mean shift bandwidth parameters.