Tracking　multiple　moving　targets　in　video　is　a　challenge　because　of　the　presence　of　noisy　video　data　and　varying　numbers　of　targets,　and　data　association　problems.　In　this　paper,　a　multi-target　visual　tracking　system　that　combines　object　detection　with　the　Gaussian　mixture　probability　hypothesis　density　filter　is　developed,　in　which　a　new　birth　intensity　estimation　method　based　on　entropy　distribution　and　coverage　rate　is　proposed.　The　birth　intensity　is　first　initialized　by　the　previously　obtained　target　states　and　measurements.　The　measurements　are　obtained　by　object　detection　and　are　classified　into　the　birth　measurements　and　the　survival　measurements.　The　currently　obtained　birth　measurements　are　then　used　to　update　the　birth　intensity.　In　the　update　stage,　the　entropy　distribution　is　incorporated　to　remove　some　noises　within　the　initialized　birth　intensity　that　are　irrelevant　to　the　birth　measurements.　The　coverage　rate　between　each　birth　intensity　component　and　the　corresponding　birth　measurement　is　computed　to　further　eliminate　the　noises.　Experiments　on　noisy　video　sequences　are　conducted　to　show　the　good　performance　of　the　proposed　visual　tracking　system.　©　2013　Elsevier　B.V.