Image-based　measurement　has　received　increasing　attention　as　it　can　substantially　reduce　the　cost　of　labor,　measurement　equipment,　and　installation　process.　Instead　of　using　optical　flow,　pattern,　or　marker　tracking　to　extract　a　displacement　signal,　in　this　study,　a　novel　noncontact　machine　learning-based　system　was　proposed　to　directly　predict　vibration　frequency　with　high　accuracy　and　good　reliability　by　using　image　sequences　acquired　from　a　single　camera.　The　performance　of　the　proposed　method　was　demonstrated　through　experiments　conducted　in　a　laboratory　and　under　real-field　conditions　and　compared　with　those　obtained　using　a　contacted　sensor.　The　vibration　frequency　prediction　results　of　the　proposed　method　are　compared　with　industry-level　vibration　sensor　results　in　the　frequency　domain,　demonstrating　that　the　proposed　method　could　predict　the　target-object-vibration　frequency　as　accurately　as　an　industry-level　vibration　sensor,　even　under　uncontrollable　real-field　conditions　with　no　additional　enhancement　or　extra　signal　processing　techniques.　However,　only　the　principal　vibration　frequency　of　a　measurement　target　is　predicted,　and　the　measurement　range　is　limited　by　the　trained　model.　Nonetheless,　if　these　limitations　are　resolved,　this　method　can　potentially　be　used　in　real　engineering　applications　in　mechanical　or　civil　structural　health　monitoring　thanks　to　the　simple　deployment　and　concise　pipeline　of　this　method.