The　spatial　clues　in　the　new　medium　of　depth　image　sequences　offer　the　potential　opportunities　to　solve　difficult　problems　in　human　detection,　such　as　varying　illumination,　approximate　appearance　and　occlusions.　Therefore,　object　detection　based　on　depth　image　sequences　has　attracted　much　attention　in　recent　years.　The　key　problem　of　human　detection　based　on　depth　images　is　the　efficient　extraction　of　discriminating　features.　Some　existing　methods　have　shown　efficient　performance　in　human　detection　in　a　given　scene,　such　as　torque　characteristics,　geodesic　features　and　histogram　features.　However,　these　methods　still　have　some　disadvantages　in　robustness,　accuracy　and　computational　complexity　because　a　depth　image　always　contains　noise　and　the　human　body　includes　detailed　structures.　In　this　paper,　we　propose　a　human　feature　representation　method,　called　CoHOD　(Co-occurrence　Histograms　of　Oriented　Depths),　which　combines　the　advantages　of　HOG　(Histograms　of　Oriented　Gradients)　and　co-occurrence　matrix　methods.　In　the　proposed　framework,　a　co-occurrence　matrix　is　used　to　represent　the　human　body　in　a　depth　image,　through　which　the　human　body　ultimately　is　efficiently　and　accurately　detected.　Experiments　are　conducted　to　demonstrate　the　effectiveness　of　our　proposed　method.　©　2014　Binary　Information　Press