Non-recurrent　traffic　incidents　(accidents,　stalled　vehicles　and　spilled　loads)　often　bring　about　traffic　congestion　and　even　secondary　accidents.　Detecting　and　positioning　them　quickly　and　accurately　has　important　significance　for　early　warning,　timely　incident-disposal　and　speedy　congestion-evacuation.　This　study　proposes　a　video-based　detecting　and　positioning　method　by　analysing　distribution　characteristics　of　traffic　states　in　a　road　segment.　Each　lane　in　the　monitored　segment　is　divided　into　a　cluster　of　cells.　Traffic　parameters　in　each　cell,　including　flow　rate,　average　travel　speed　and　average　space　occupancy,　are　obtained　by　detecting　and　tracking　traffic　objects　(vehicles　and　spilled　loads).　On　the　basis　of　the　parameters,　traffic　states　in　the　cells　are　judged　via　a　fuzzy-identification　method.　For　each　congested　cell,　a　feature　vector　is　constructed　by　taking　its　state　together　with　states　of　its　upstream　and　downstream　neighbouring　cells　in　the　same　lane.　Then,　a　support　vector　machine　classifier　is　trained　to　detect　incident　point.　If　a　cell　is　judged　to　be　corresponding　to　an　incident　point　at　least　for　two　successive　time　periods,　an　incident　is　detected　and　its　position　is　calculated　based　on　the　identity　number　of　the　cell.　Experiments　prove　the　efficiency　and　practicability　of　the　proposed　method.