In　this　paper,　a　novel　and　robust　method　which　exploits　the　spatiotemporal　context　for　orderless　and　blurred　visual　tracking　is　presented.　This　lets　the　tracker　adapt　to　both　rigid　and　deformable　objects　on-line　even　if　the　image　is　blurred.　We　observe　that　a　RGB　vector　of　an　image　which　is　resized　into　a　small　fixed　size　can　keep　enough　useful　information.　Based　on　this　observation　and　computational　reasons,　we　propose　to　resize　the　windows　of　both　template　and　candidate　target　images　into　2×2　and　use　Euclidean　Distance　to　compute　the　similarity　between　these　two　RGB　image　vectors　for　the　preliminary　screening.　We　then　apply　spatio-temporal　context　based　on　Bayesian　framework　to　further　compute　a　confidence　map　for　obtaining　the　best　target　location.　Experimental　results　on　challenging　video　sequences　in　MATLAB　without　code　optimization　show　the　proposed　tracking　method　outperforms　eight　state-of-the-art　methods.　©　Springer　International　Publishing　Switzerland　2015.