Accident　detection　in　surveillance　or　dashcam　videos　is　a　common　task　in　the　field　of　traffic　accident　analysis　by　using　videos.　However,　as　accidents　occur　sparsely　and　randomly　in　the　real　world,　the　data　records　are　more　scarce　than　the　training　data　for　standard　detection　tasks　such　as　object　detection　or　instance　detection.　Moreover,　the　limited　and　diverse　accident　data　makes　it　more　difficult　to　model　the　accident　pattern　for　fine-grained　accident　detection　tasks　analyzing　the　accident　in　detail.　Extra　prior　information　should　be　introduced　in　the　tasks　such　as　the　common　vision　feature　which　could　offer　relatively　effective　information　for　many　vision　tasks.　The　big　model　could　generate　the　common　vision　feature　by　training　on　abundant　data　and　consuming　a　lot　of　computing　time　and　resources.　Even　though　the　accident　video　data　is　special,　the　big　model　could　also　extract　common　vision　features.　Thus,　in　this　paper,　we　propose　to　apply　knowledge　distillation　to　fine-grained　accident　detection　which　analyzes　the　spatial　temporal　existence　and　severity　for　solving　the　issues　of　complex　computing　(distillation　to　the　small　model)　and　keeping　good　performance　under　limited　accident　data.　Knowledge　distillation　could　offer　extra　general　vision　feature　information　from　the　pre-trained　big　model.　Common　knowledge　distillation　guides　the　student　network　to　learn　the　same　representations　from　the　teacher　network　by　logit　mimicking　or　feature　imitation.　However,　single-level　distillation　could　only　focus　on　one　aspect　of　mimicking　classification　logit　or　deep　features.　Multiple　tasks　with　different　focuses　are　required　for　fine-grained　accident　detection,　such　as　multiple　accident　classification,　temporal-spatial　accident　region　detection,　and　accident　severity　estimation.　Thus　in　this　paper,　multiple-level　distillation　is　proposed　for　the　different　modules　to　generate　the　unified　video　feature　concerning　all　the　tasks　in　fine-grained　accident　detection　analysis.　The　various　experimental　results　on　a　fine-grained　accident　detection　dataset　which　provides　more　detailed　annotations　of　accidents　demonstrate　that　our　method　could　effectively　model　the　video　feature　for　multiple　tasks.