With　the　economic　and　social　development　and　the　substantial　improvement　of　material　conditions,　the　generation　of　domestic　waste　has　grown　rapidly　and　has　become　a　constraint　factor　for　the　development　of　new　urbanization.　In　the　past　few　years,　research　on　the　domestic　waste　industry　has　been　limited　to　intelligent　waste　sorting,　neglecting　the　role　of　intelligent　management　of　waste　storage　sites.　To　relieve　it,　We　propose　a　deep　learning-based　multi-task　network　for　intelligent　management　of　garbage　deposit　points,　which　combines　algorithms　such　as　YoloV5,Deepsort,　Insightface,　and　Openpose　to　achieve　waste　bin　detection,　waste　bin　status　recognition　and　analysis,　face　recognition,　action　recognition,　and　multiple　object　tracking　based　on　real-time　surveillance　video.　Besides,　we　propose　a　new　dataset　named　Waste　Bin　Status,　which　provides　a　meaningful　addition　to　the　existing　field　of　waste　bin　identification.　Experiments　on　WBS　dataset　validate　that　our　method　is　superior　to　other　methods　for　garbage　point　status　identification.　Moreover,　our　network　is　trained　to　work　with　different　scenarios　of　garbage　deposits,　demonstrating　state-of-the-art　performance　in　real-world　tests.　©　2021　IEEE.