Live　video　platforms　have　attracted　many　active　streamers　and　daily　users,　and　quickly　understanding　live　video　streaming　scenes　is　crucial　for　ensuring　the　clean　and　healthy　cyberspace.　Video　logo　often　appear　in　live　video　and　can　serve　as　key　clues　for　understanding　video　streaming　scenes.　Due　to　the　presence　of　the　jitter　scenes,　as　well　as　blur　and　deformed　logos　in　live　streaming　scenes,　we　propose　a　spatial-channel　specific　snake-yolov8　for　video　logo　detection　in　live　streaming　scenes.　First,　we　design　an　involutional　fusion　layer　to　fuse　interframe　logo　features　for　jitter　scenes.　Then,　the　spatial-channel　specific　involution　is　proposed　to　extract　spatial　and　channel　context,　to　adapt　to　blurring　logos　during　logo　detection.　Finally,　to　cope　with　deformable　logos　in　live　steaming　scenes,　we　embed　snake　deformation　convolution　into　spatial-channel　specific　involution.　Experimental　results　show　that　under　inference　efficiency　of　39.8　FPS,　the　mAP　of　the　proposed　method　reaches　69.7%　on　LogoDet-3K,　and　53.0%　on　self-built　BJUT-VLD,　which　has　certain　effectiveness　and　superiority　for　video　logo　detection　in　live　streaming　scenes.　©　2024　ACM.