Multi-target　tracking　in　video　is　a　challenge　due　to　noisy　video　data,　varying　number　of　targets,　and　the　data　association　problems.　In　this　paper,　a　multi-target　visual　tracking　system　that　incorporates　object　detection　with　the　Gaussian　mixture　PHD　filter　is　developed.　The　main　contribution　of　this　paper　is　to　propose　a　new　birth　intensity　online　estimation　method　that　based　on　the　entropy　distribution　and　the　coverage　rate.　First,　the　birth　intensity　is　initialized　by　using　the　previously　obtained　targets＇　states　and　measurements.　The　measurements　are　obtained　by　object　detection　and　classified　into　the　birth　measurements　and　the　survival　measurements.　Then　it　is　updated　according　to　the　currently　obtained　birth　measurements.　In　the　update　stage,　the　instability　of　the　entropy　distribution　is　applied　to　remove　components　like　noises　within　the　birth　intensity　which　are　irrelevant　with　the　currently　obtained　birth　measurements.　And　the　coverage　rate　between　each　birth　intensity　component　and　corresponding　birth　measurement　is　computed　to　further　eliminate　the　noises.　Finally,　experiments　are　implemented　to　show　the　performance　of　the　proposed　visual　tracking　system,　especially　to　show　the　good　performance　for　tracking　the　newborn　targets.　©　2012　IEEE.