With　the　development　of　emerging　computer　vision　applications,　there　is　an　increasing　demand　for　capturing　the　scenes　with　high-speed　motion.　Recently,　a　novel　retina-inspired　spike　camera　has　shown　great　potential　for　recording　the　dynamic　scenes　at　high　temporal　resolution.　Different　from　the　conventional　digital　cameras　that　capture　the　visual　scene　by　a　single　snapshot,　the　spike　camera　monitors　the　incoming　light　persistently,　with　each　pixel　producing　a　continuous　stream　of　spikes.　Recovering　the　motion　process　from　the　spike　data　sequence　is　an　important　problem　to　study　for　the　spike　camera,　as　it　is　the　foundation　of　many　other　tasks,　such　as　image　reconstruction,　object　tracking　and　object　detection.　In　this　paper,　we　carefully　analyze　the　characteristics　of　spike　data　and　develop　a　motion　estimation　algorithm　to　recover　the　continuous　high-speed　motion　process　from　the　spike　camera　data　sequence.　Based　on　the　assumption　that　the　spike　intervals　passed　by　the　same　motion　trajectories　usually　have　the　similar　spike　densities,　we　establish　a　data　term　constraint　to　model　the　temporal　consistency　of　spike　intervals.　In　addition,　we　integrate　a　local　smoothness　constraint　with　the　proposed　data　term　constraint　to　further　improve　the　estimation　accuracy.　Experimental　results　demonstrate　that　our　proposed　algorithm　can　recover　high-speed　motion　process　from　the　captured　spike　data,　and　the　recovered　motion　information　is　beneficial　for　i　mage　reconstruction.　©　2020　IEEE.