Tracking　multiple　moving　targets　in　a　video　is　a　challenge　because　of　several　factors,　including　noisy　video　data,　varying　number　of　targets,　and　mutual　occlusion　problems.　The　Gaussian　mixture　probability　hypothesis　density　(GM-PHD)　filter,　which　aims　to　recursively　propagate　the　intensity　associated　with　the　multi-target　posterior　density,　can　overcome　the　difficulty　caused　by　the　data　association.　This　paper　develops　a　multi-target　visual　tracking　system　that　combines　the　GM-PHD　filter　with　object　detection.　First,　a　new　birth　intensity　estimation　algorithm　based　on　entropy　distribution　and　coverage　rate　is　proposed　to　automatically　and　accurately　track　the　newborn　targets　in　a　noisy　video.　Then,　a　robust　game-theoretical　mutual　occlusion　handling　algorithm　with　an　improved　spatial　color　appearance　model　is　proposed　to　effectively　track　the　targets　in　mutual　occlusion.　The　spatial　color　appearance　model　is　improved　by　incorporating　interferences　of　other　targets　within　the　occlusion　region.　Finally,　the　experiments　conducted　on　publicly　available　videos　demonstrate　the　good　performance　of　the　proposed　visual　tracking　system.