WildGEN: Long-horizon Trajectory Generation for Wildlife
Ali Al-Lawati Elsayed Eshra Prasenjit Mitra
Trajectory generation is an important concern in pedestrian, vehicle, and
wildlife movement studies. Generated trajectories help enrich the training
corpus in relation to deep learning applications, and may be used to facilitate
simulation tasks. This is especially significant in the wildlife domain, where
the cost of obtaining additional real data can be prohibitively expensive,
time-consuming, and bear ethical considerations. In this paper, we introduce
WildGEN: a conceptual framework that addresses this challenge by employing a
Variational Auto-encoders (VAEs) based method for the acquisition of movement
characteristics exhibited by wild geese over a long horizon using a sparse set
of truth samples. A subsequent post-processing step of the generated
trajectories is performed based on smoothing filters to reduce excessive
wandering. Our evaluation is conducted through visual inspection and the
computation of the Hausdorff distance between the generated and real
trajectories. In addition, we utilize the Pearson Correlation Coefficient as a
way to measure how realistic the trajectories are based on the similarity of
clusters evaluated on the generated and real trajectories.