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Large-scale capture of hidden fluorescent labels for training generalizable markerless motion capture models
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Large-scale capture of hidden fluorescent labels for training generalizable markerless motion capture models
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Large-scale capture of hidden fluorescent labels for training generalizable markerless motion capture models
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Journal Article
Large-scale capture of hidden fluorescent labels for training generalizable markerless motion capture models
2023
Overview
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.
Deep learning-based models for tracking behavior are often constrained by manual annotation. Here, authors present GlowTrack, an approach using fluorescence to generate large and diverse training sets that improve model robustness and tracking coverage.
