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Image-based tracking software are regarded as valuable tools in collective animal behaviour studies. For such operations, image preprocessing is a prerequisite, and the users are required to build an appropriate image-processing pipeline for extracting the shape of animals. Even if the users successfully design an image-processing pipeline, unexpected noise in the video frame may significantly reduce the tracking accuracy in the tracking step. To address these issues, we propose UMATracker (Useful Multiple Animal Tracker), which supports flexible image preprocessing by visual programming, multiple tracking algorithms and a manual tracking error-correction system. UMATracker employs a visual programming user interface, wherein the user can intuitively design an image-processing pipeline. Moreover, the software also enables the user to visualize the effect of image processing. We implement four different tracking algorithms to enable the users to choose the most suitable algorithm. In addition, UMATracker provides a manual correction tool for identifying and correcting tracking errors.
Yamanaka et al. (Mon,) studied this question.