The　task　of　identifying　moving　forces　presents　a　challenging　inverse　problem　for　bridge　health　monitoring　and　condition　assessment.　Existing　methods　for　identifying　moving　loads　rely　on　complex　structural　response　measurement　equipment,　resulting　in　inefficiency　and　elevated　costs.　To　address　these　challenges,　this　study　introduces　an　innovative　intelligent　identification　method　for　moving　forces　on　bridges　using　visual　perception　techniques.　The　proposed　method　utilizes　cameras　to　capture　displacement　changes　at　specific　points　on　the　bridge　surface　to　reconstruct　moving　forces　in　the　time　domain.　Initially,　numerical　simulations　were　conducted　to　ascertain　the　feasibility　and　robustness　of　identifying　moving　forces　based　solely　on　the　structural　displacement　response.　This　phase　also　investigates　the　influence　of　different　measurement　point　combinations　on　identification　accuracy.　Subsequently,　the　accuracy　of　the　proposed　method　in　measuring　the　dynamic　displacement　response　of　the　structure　was　carefully　evaluated　by　an　outdoor　shaking　table　test.　Furthermore,　a　meticulously　designed　simply　supported　beam　model　was　fabricated,　followed　by　the　execution　of　precise　vehicle-bridge　coupling　dynamic　experiments.　This　phase　rigorously　validates　the　effectiveness　and　accuracy　of　the　proposed　moving　force　identification　method　at　varying　vehicle　speeds.　The　obtained　results　substantiate　the　proposed　approach＇s　capability　not　only　to　comprehensively　capture　bridge　response　data　for　full-area　monitoring　but　also　to　consistently　and　reliably　identify　information　on　moving　vehicle　forces.　This　study　underscores　a　pioneering　application　of　intelligent　visual　perception　technology　in　the　field　of　identifying　moving　forces.　The　introduced　noncontact　intelligent　identification　method　is　an　effective　solution　for　monitoring　moving　forces　on　bridges,　which　has　a　wide　range　of　applications　in　the　future.　©　2024　Elsevier　Ltd