Night　target　tracking　usually　fails　due　to　various　reasons　such　as　insufficient　light,　appearance　change,　motion　blur,　illumination　variation,　and　deformation.　Because　infrared　(IR)　and　visible　video　data　provides　complementary　information　that　can　be　utilized　suitably　and　efficiently,　we　explore　a　novel　framework　by　combining　correlation　filter-based　visible　tracking　and　Markov　chain　Monte　Carlo　(MCMC)-based　IR　tracking　to　overcome　these　challenges.　In　this　framework,　the　two　types　of　videos　are　asynchronous,　and　the　frame　rate　of　visible　video　is　several　times　faster　than　that　of　IR　video.　Visible　video　is　first　used　for　location　and　scale　estimation　by　solving　a　ridge　regression　problem　efficiently　in　the　correlation　filter　domain.　When　recording　IR　data,　we　use　a　uniquely　designed　feature　shape　context　descriptor　for　the　best　location　and　scale　estimation　of　an　IR　video　target　by　using　the　MCMC　particle　filter.　Then,　we　use　candidate　region　location-scale　fusion　rules　for　the　final　target　tracking　update.　Meanwhile,　we　build　an　accurately　labeled　IR　and　visible　target　tracking　dataset　for　experiments.　The　result　shows　that　the　performance　of　our　proposed　approach　is　better　than　the　state-of-the-art　trackers　for　night　target　tracking,　and　our　approach　can　significantly　improve　re-tracking　performance　when　there　is　the　drift.