Accurately　modeling　and　predicting　the　visual　attention　behavior　of　human　viewers　can　help　a　video　analysis　algorithm　find　interesting　regions　by　reducing　the　search　effort　of　tasks,　such　as　object　detection　and　recognition.　In　recent　years,　a　great　number　and　variety　of　visual　attention　models　for　predicting　the　direction　of　gaze　on　images　and　videos　have　been　proposed.　When　a　human　views　video,　the　motions　of　both　objects　in　the　video　and　of　the　camera　greatly　affect　the　distribution　of　visual　fixations.　Here　we　develop　models　that　lead　to　motion　features　that　are　extracted　from　videos　and　used　in　a　new　video　saliency　detection　method　called　spatial-temporal　weighted　dissimilarity　(STWD).　To　achieve　efficiency,　frames　are　partitioned　into　blocks　on　which　saliency　calculations　are　made.　Two　spatial　features　are　defined　on　each　block,　termed　spatial　dissimilarity　and　preference　difference,　which　are　used　to　characterize　the　spatial　conspicuity　of　each　block.　The　motion　features　extracted　from　each　block　are　simple　differences　of　motion　vectors　between　adjacent　frames.　Finally,　the　spatial　and　motion　features　are　used　to　generate　a　saliency　map　on　each　frame.　Experiments　on　three　public　video　datasets　containing　185　video　clips　and　corresponding　eye　traces　revealed　that　the　proposed　saliency　detection　method　is　highly　competitive　with,　and　delivers　better　performance　than　state-of-the-art　methods.　(C)　2015　Elsevier　B.V.　All　rights　reserved.