Information　on　the　vibration　characteristics　of　machinery　or　civil　structures　is　crucial　in　identifying　or　diagnosing　potential　malfunctions　or　faults.　The　use　of　cameras　in　vibration　frequency　measurement　enables　the　acquisition　of　spatially　high-density　information　of　a　distant　measurement　target.　Cameras　can　be　used　for　remote　monitoring　or　as　contact-less　inspection　sensors　for　machinery　or　civil　structures　owing　to　their　low　cost,　high　availability,　and　ease　of　installation.　However,　this　requires　complex　image　processing　and　signal　processing　algorithms　to　interpret　the　vibration　frequency　from　image　data.　This　paper　proposes　an　image-　and　machine-learning-based　method　to　measure　the　vibration　frequency,　in　which　long　short-term　memory-recurrent　neural　networks　(LSTM-RNNs)　and　multi-target　learning　are　used　to　predict　the　vibration　frequency　components　directly.　The　results　of　the　proposed　method　are　compared　against　the　results　obtained　using　an　accelerometer　and　eddy　current　sensor　in　the　frequency　domain.　The　methodology　was　applied　in　forced　vibration　and　free　vibration　laboratory　experiments　and　real-world　structure　experiments　including　a　membrane　structure　and　a　cable　bridge.　The　experimental　results　show　that　the　proposed　method　is　robust　and　accurate.　Proposals　for　future　applications　and　research　directions　of　this　methodology　and　limitations　and　challenges　in　real-world　implementations　are　presented.　©　2019