Target　tracking　is　one　of　the　important　tasks　in　computer　vision.　It　aims　to　detect　and　track　one　or　more　particular　objects　in　videos.　The　target　and　background　may　change　in　the　process　of　tracking.　In　order　to　solve　this　problem,　this　paper　proposes　an　online　learning　target　tracking　method　based　on　extreme　learning　machine　(ELM).　First　of　all,　we　capture　the　target　and　background　regions　in　the　first　few　frames　of　video　and　extract　the　histograms　of　oriented　gradients　(HOG)　features　of　regions　into　ELM.　Secondly,　using　the　method　of　sliding　window　to　detect　the　candidate　region　after　loading　a　new　image.　Finally,　according　to　the　tracking　result,　the　classifier　can　be　updated　for　online　learning.　In　order　to　promote　the　detection　speed,　this　method　predicts　a　region　in　the　current　frame　according　to　the　target　position　of　the　previous　frame.　The　predicted　region　is　called　the　candidate　region.　Experiment　results　have　shown　that　this　proposed　method　not　only　achieves　high　accuracy　but　also　can　adapt　to　the　changes　of　target　and　background.　©　2016　IEEE.