Event　cameras,　or　dynamic　vision　sensors,　have　recently　achieved　success　from　fundamental　vision　tasks　to　high-level　vision　researches.　Due　to　its　ability　to　asynchronously　capture　light　intensity　changes,　event　camera　has　an　inherent　advantage　to　capture　moving　objects　in　challenging　scenarios　including　objects　under　low　light,　high　dynamic　range,　or　fast　moving　objects.　Thus　event　camera　are　natural　for　visual　object　tracking.　However,　the　current　event-based　trackers　derived　from　RGB　trackers　simply　modify　the　input　images　to　event　frames　and　still　follow　conventional　tracking　pipeline　that　mainly　focus　on　object　texture　for　target　distinction.　As　a　result,　the　trackers　may　not　be　robust　dealing　with　challenging　scenarios　such　as　moving　cameras　and　cluttered　foreground.　In　this　paper,　we　propose　a　distractor-aware　event-based　tracker　that　introduces　transformer　modules　into　Siamese　network　architecture　(named　DANet).　Specifically,　our　model　is　mainly　composed　of　a　motion-aware　network　and　a　target-aware　network,　which　simultaneously　exploits　both　motion　cues　and　object　contours　from　event　data,　so　as　to　discover　motion　objects　and　identify　the　target　object　by　removing　dynamic　distractors.　Our　DANet　can　be　trained　in　an　end-to-end　manner　without　any　post-processing　and　can　run　at　over　80　FPS　on　a　single　V100.　We　conduct　comprehensive　experiments　on　two　large　event　tracking　datasets　to　validate　the　proposed　model.　We　demonstrate　that　our　tracker　has　superior　performance　against　the　state-of-the-art　trackers　in　terms　of　both　accuracy　and　efficiency.