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Reconstruction of Fragmented Trajectories of Collective Motion using Hadamard Deep Autoencoders
[article]
2021
arXiv
pre-print
Learning dynamics of collectively moving agents such as fish or humans is an active field in research. Due to natural phenomena such as occlusion and change of illumination, the multi-object methods tracking such dynamics might lose track of the agents where that might result fragmentation in the constructed trajectories. Here, we present an extended deep autoencoder (DA) that we train only on fully observed segments of the trajectories by defining its loss function as the Hadamard product of a
arXiv:2110.10428v1
fatcat:akfo4kjkwrgu3bvcn5b35dszru