In　the　vehicle　security　inspection　system　design　based　on　machine　vision,　a　subspace　clustering　algorithm　via　two　granularity　optical　flow　manifold　learning　was　proposed　to　detect　automobile　wiper　swing　angle　amplitude　in　order　to　avoid　the　defect　that　the　optical　flow　trajectory　is　too　sparse　caused　by　the　complex　background　of　glass.Firstly,　the　full-length　wiper　LDOF　variational　optical　flow　trajectory　was　used　as　coarse-granularity　optical　flow　to　perform　sparse　subspace　clustering　to　obtain　reliable　seed　sample.Then　the　temporal-spatial　similarity　topography　graph　of　dense　and　fine　granularity　optical　flow　and　coarse-granularity　optical　flow　was　constructed,　and　the　harmonic　function　was　used　to　relax　the　neighbor　trajectory　label　of　the　seed＇s　to　the　Gaussian　random　field　for　semi-supervised　label　diffusion　to　obtain　a　dense　wiper　motion　region　for　further　RANSAC　line　fitting　and　swing　angle　calculation.Finaly,　the　algorithm　module　was　encapsulated　by　ocx　plugin　and　embeded　into　the　vehicle　tracking　module　in　the　form　of　callback　function　for　synchronization.Security　check　video　of　153　trains　of　4　types　of　vehicles　under　6　different　illuminances　was　collected.The　videos　was　used　to　analyze　and　compare　the　accuracy　of　fitting　and　swing　angle　of　two　granularity　optical　flow　manifold　learning　algorithms.The　experiment　results　show　that　the　accuracy　of　fitting　and　swing　angle　of　the　new　algorithm　can　reach　more　than　85%,　showing　a　broad　application　and　promotion　prospect.　©　2020,　Editorial　Department,　Journal　of　South　China　University　of　Technology.　All　right　reserved.