biostudies-literatureButler DJU.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and StrokePew Charitable TrustsNINDS NIH HHSMcKnight FoundationSalk Institute, Searle Scholars ProgramUCSD CMG Training Program, Jesse and Caryl Philips Foundation Award5866https://www.ebi.ac.uk/biostudies/studies/S-EPMC10522643biostudies-literatureUnknown14(1)Deep learning-based markerless tracking has revolutionized studies of animal behavior. Yet the generalizability of trained models tends to be limited, as new training data typically needs to be generated manually for each setup or visual environment. With each model trained from scratch, researchers track distinct landmarks and analyze the resulting kinematic data in idiosyncratic ways. Moreover, due to inherent limitations in manual annotation, only a sparse set of landmarks are typically labeled. To address these issues, we developed an approach, which we term GlowTrack, for generating orders of magnitude more training data, enabling models that generalize across experimental contexts. We describe: a) a high-throughput approach for producing hidden labels using fluorescent markers; b) a multi-camera, multi-light setup for simulating diverse visual conditions; and c) a technique for labeling many landmarks in parallel, enabling dense tracking. These advances lay a foundation for standardized behavioral pipelines and more complete scrutiny of movement.Nature communicationsLarge-scale capture of hidden fluorescent labels for training generalizable markerless motion capture models.PMC10522643DP2 NS105555NS112959NS088193R00 NS088193R01 NS111479NS128898RF1 NS128898NS111479NS105555Butler DJAzim EKeim APRay SfalseLarge-scale capture of hidden fluorescent labels for training generalizable markerless motion capture models.Deep learning-based markerless tracking has revolutionized studies of animal behavior. Yet the generalizability of trained models tends to be limited, as new training data typically needs to be generated manually for each setup or visual environment. With each model trained from scratch, researchers track distinct landmarks and analyze the resulting kinematic data in idiosyncratic ways. Moreover, due to inherent limitations in manual annotation, only a sparse set of landmarks are typically labeled. To address these issues, we developed an approach, which we term GlowTrack, for generating orders of magnitude more training data, enabling models that generalize across experimental contexts. We describe: a) a high-throughput approach for producing hidden labels using fluorescent markers; b) a multi-camera, multi-light setup for simulating diverse visual conditions; and c) a technique for labeling many landmarks in parallel, enabling dense tracking. These advances lay a foundation for standardized behavioral pipelines and more complete scrutiny of movement.2023-01-01T00:00:00Z2023 Sep2025-04-04T08:23:43.399Z2025-02-19T04:08:09.048ZS-EPMC105226433775212310.1038/s41467-023-41565-3