Advanced　wireless　imaging　sensors　and　cloud　data　storage　contribute　to　video　surveillance　by　enabling　the　generation　of　large　amounts　of　video　footage　every　second.　Consequently,　surveillance　videos　have　become　one　of　the　largest　sources　of　unstructured　data.　Because　multi-scenario　surveillance　videos　are　often　continuously　produced,　using　these　videos　to　detect　moving　objects　is　challenging　for　the　conventional　moving　object　detection　methods.　This　paper　presents　a　novel　model　that　harnesses　both　sparsity　and　low-rankness　with　contextual　regularization　to　detect　moving　objects　in　multi-scenario　surveillance　data.　In　the　proposed　model,　we　consider　moving　objects　as　a　contiguous　outlier　detection　problem　through　the　use　of　low-rank　constraint　with　contextual　regularization,　and　we　construct　dedicated　backgrounds　for　multiple　scenarios　using　dictionary　learning-based　sparse　representation,　which　ensures　that　our　model　can　be　effectively　applied　to　multi-scenario　videos.　Quantitative　and　qualitative　assessments　indicate　that　the　proposed　model　outperforms　existing　methods　and　achieves　substantially　more　robust　performance　than　the　other　state-of-the-art　methods.