Abstract
Deepening our understanding of animals’ collective motions represents a multidisciplinary goal. Yet, quantifying the motions of hundreds of animals in the laboratory and nature posits a fundamental challenge for digital image processing: How do we track each object out of the crowd while allowing them to move freely in a three-dimensional (3D) domain? Here, we present a simple tracking strategy to reconstruct 3D trajectories with the aid of a mirror, even if moving objects experience occlusion. We explain the method using synthetically generated datasets and apply it to measure collective motions of phototactic zooplankton, Daphnia magna, swimming in a lab-scale aquarium at intermediate Reynolds numbers, 1<Re<13 . The method enables measuring statistics of characteristic features of D. magna swarm, including sinking velocities and flapping frequencies. Beyond the lab-scale animal tracking, we foresee further implementations of the method to study wild animals freely behaving in 3D environments irrespective of their species.
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