Human-following　ability　enables　mobile　robots　to　cooperate　with　human　beings　smartly.　The　kernelized　correlation　filter　(KCF)　was　typically　adopted　as　a　human　tracker　during　the　human-following　process　due　to　its　advantages　of　fast　speed　and　high　precision.　However,　it　is　easy　to　drift　away　owing　to　the　changes　of　human　pose.　In　this　study,　we　proposed　an　efficient　human-following　method　by　fusion　of　KCF　and　depth　information　for　the　mobile　robot.　The　target　human　was　separated　from　the　background　regions　based　on　the　seeded　region　growing.　KCF　tracker　outputs　and　segmentation　results　were　fused　by　using　the　adaptive　weighted　fusion　method.　The　weighting　factors　were　adjusted　based　on　the　tracking　quality　indicators,　which　reflect　the　tracking　reliability　of　KCF　and　the　distribution　of　depth　information.　Experimental　results　verify　the　effectiveness　of　the　proposed　method　in　terms　of　tracking　speed　and　stability.　The　center　location　error　of　all　frames　was　less　than　48　pixels　and　the　bounding　box　overlap　rate　of　all　frames　was　larger　than　0.62.　©　2019　IEEE.