Modern　video　surveillance　benefits　greatly　from　advanced　wireless　imaging　sensors　and　cloud　data　storage;　thus,　a　vast　amount　of　data　is　generated　every　second.　Surveillance　videos　have　thus　become　one　of　the　biggest　sources　of　unstructured　data.　Because　a　vast　amount　of　surveillance　videos　is　continuously　and　quickly　produced　at　multiple　locations,　moving　object　detection　in　such　a　vast　amount　of　these　videos　by　using　traditional　detection　methods　is　a　challenging　task.　This　paper　presents　a　novel　model　that　detects　moving　objects　from　such　data　sets　based　on　low-rank　representation　with　contextual　regularization.　Quantitative　and　qualitative　assessments　indicated　that　the　proposed　model　significantly　outperformed　existing　state-of-the-art　moving　object　detection　methods.