Existing　approaches　still　face　issues　such　as　the　lack　of　spatial-temporal　information　and　target　prior　information,　as　well　as　low　detection　efficiency　when　dealing　with　small　infrared　(IR)　target　detection　tasks　under　heterogeneous　backgrounds.　To　address　the　aforementioned　issues,　this　article　presents　a　low　rank　tensor　approximation　with　saliency　prior　(LRTA-SP)　approach,　where　the　holistic　spatiotemporal　tensor　model　(STTM)　is　constructed　by　combining　spatiotemporal　related　prior　information　of　IR　videos　with　target　saliency　prior　information.　Firstly,　benefiting　by　the　subspace　optimization　theory,　the　target　and　the　background　can　be　separated　into　a　sparse　and　a　low　rank　component,　respectively.　Crucially,　the　proposed　LRTA-SP　optimization　approach　updates　the　background　tensor　on　a　tangent　space　to　accelerate　the　above　optimization　process,　greatly　reducing　the　computational　complexity　of　low　rank　projection　while　improving　the　detection　efficiency　of　our　model.　Also,　applying　a　multi-rank　constrain　in　low　rank　regularization　term　helps　to　adaptively　preserve　important　information　in　the　frequency　domain.　Furthermore,　a　prior　weight　tensor　containing　target　saliency　information　is　provided　in　sparse　regularization　term　to　preserve　contextual　information　during　the　optimization　process.　Finally,　an　alternating　projection-based　algorithm　framework　is　designed　to　robustly　separate　sparse　targets　and　low　rank　backgrounds.　The　effectiveness　and　superiority,　especially　the　detection　efficiency,　of　the　proposed　LRTA-SP　technology　to　similar　detection　technologies　are　validated　on　six　real　IR　videos　under　various　scenarios.　©　1965-2011　IEEE.