A　novel　algorithm　for　vehicle　average　velocity　detection　through　automatic　and　dynamic　camera　calibration　based　on　dark　channel　in　homogenous　fog　weather　condition　is　presented　in　this　paper.　Camera　fixed　in　the　middle　of　the　road　should　be　calibrated　in　homogenous　fog　weather　condition,　and　can　be　used　in　any　weather　condition.　Unlike　other　researches　in　velocity　calculation　area,　our　traffic　model　only　includes　road　plane　and　vehicles　in　motion.　Painted　lines　in　scene　image　are　neglected　because　sometimes　there　are　no　traffic　lanes,　especially　in　un-structured　traffic　scene.　Once　calibrated,　scene　distance　will　be　got　and　can　be　used　to　calculate　vehicles　average　velocity.　Three　major　steps　are　included　in　our　algorithm.　Firstly,　current　video　frame　is　recognized　to　discriminate　current　weather　condition　based　on　area　search　method　(ASM).　If　it　is　homogenous　fog,　average　pixel　value　from　top　to　bottom　in　the　selected　area　will　change　in　the　form　of　edge　spread　function　(ESF).　Secondly,　traffic　road　surface　plane　will　be　found　by　generating　activity　map　created　by　calculating　the　expected　value　of　the　absolute　intensity　difference　between　two　adjacent　frames.　Finally,　scene　transmission　image　is　got　by　dark　channel　prior　theory,　camera＇s　intrinsic　and　extrinsic　parameters　are　calculated　based　on　the　parameter　calibration　formula　deduced　from　monocular　model　and　scene　transmission　image.　In　this　step,　several　key　points　with　particular　transmission　value　for　generating　necessary　calculation　equations　on　road　surface　are　selected　to　calibrate　the　camera.　Vehicles＇　pixel　coordinates　are　transformed　to　camera　coordinates.　Distance　between　vehicles　and　the　camera　will　be　calculated,　and　then　average　velocity　for　each　vehicle　is　got.　At　the　end　of　this　paper,　calibration　results　and　vehicles　velocity　data　for　nine　vehicles　in　different　weather　conditions　are　given.　Comparison　with　other　algorithms　verifies　the　effectiveness　of　our　algorithm.　©　2013　Institute　of　Automation,　Chinese　Academy　of　Sciences　and　Springer-Verlag　Berlin　Heidelberg.