The　tracking–learning–detection　(TLD)　algorithm　applied　in　the　home　environment　can　effectively　improve　the　tracking　robustness.　However,　it　has　the　problems　of　single　target　tracking　and　poor　selection　of　feature　points.　This　study　proposed　a　dynamic　target　tracking　method　based　on　corner　enhancement　with　Markov　decision　process　(MDP)　model.　The　MDP　target　tracking　method　is　adopted　to　change　a　multi-target　tracking　problem　into　a　strategy　problem　based　on　MDP　model,　in　which　one　MDP　model　represents　the　life　cycle　of　a　target,　and　multiple　targets　are　represented　by　multiple　MDP　models.　In　the　tracking　process,　the　strong　corners　generated　by　the　Shi-Tomasi　corner　method　are　used　to　replace　the　feature　points　generated　by　the　traditional　TLD　algorithm　at　intermediate　intervals,　which　makes　the　target　feature　points　more　stable　during　the　tracking　process.　The　similarity　function　learning　for　data　association　is　equivalent　to　the　learning　of　the　MDP　strategy,　in　which　the　reinforcement　learning　method　is　used　and　has　double　advantages　of　both　online　and　offline　learning.　The　tracking　experiments　with　different　data　sets　are　performed,　and　the　results　show　that　dynamic　target　tracking　algorithm　based　on　the　corner　enhancement　with　MDP　has　both　good　tracking　performance　and　good　anti-interference　capability.　©　2018　Institution　of　Engineering　and　Technology.　All　rights　reserved.