Video　panoramic　image　stitching　is　in　general　challenging　because　there　is　small　overlapping　between　original　images,　and　stitching　processes　are　therefore　extremely　time　consuming.　We　present　a　new　algorithm　in　this　paper.　Our　contribution　can　be　summarized　as　a　multiple-constraint　corner　matching　process　and　the　resultant　faster　image　stitching.　The　traditional　Random　Sample　Consensus　(RANSAC)　algorithm　is　inefficient,　especially　when　stitching　a　large　number　of　images　and　when　these　images　have　quite　similar　features.　We　first　filter　out　many　inappropriate　corners　according　to　their　position　information.　An　initial　set　of　candidate　matching-corner　pairs　is　then　generated　based　on　grayscales　of　adjacent　regions　around　each　corner.　Finally　we　apply　multiple　constraints,　e.g.,　their　midpoints,　distances,　and　slopes,　on　every　two　candidate　pairs　to　remove　incorrectly　matched　pairs.　Consequently,　we　are　able　to　significantly　reduce　the　number　of　iterations　needed　in　RANSAC　algorithm　so　that　the　panorama　stitching　can　be　performed　in　a　much　more　efficient　manner.　Experimental　results　demonstrate　that　(i)　our　corner　matching　is　three　times　faster　than　normalized　cross-correlation　function　(NCC)　rough　match　in　traditional　RANSAC　algorithm　and　(ii)　panoramas　generated　from　our　algorithm　feature　a　smooth　transition　in　overlapping　image　areas　and　satisfy　human　visual　requirements.