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2026-02-24 22:34:22
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Non-Probability Sampling Network for Stochastic Human Trajectory Prediction | Project Page Non-Probability Sampling Network for Stochastic Human Trajectory Prediction Inhwan Bae , Jin-Hwi Park and Hae-Gon Jeon* Computer Vision and Pattern Recognition 2022 Project Paper Code Abstract Capturing multimodal natures is essential for stochastic pedestrian trajectory prediction, to infer a finite set of future trajectories. The inferred trajectories are based on observation paths and the latent vectors of potential decisions of pedestrians in the inference step. However, stochastic approaches provide varying results for the same data and parameter settings, due to the random sampling of the latent vector. In this paper, we analyze the problem by reconstructing and comparing probabilistic distributions from prediction samples and socially-acceptable paths, respectively. Through this analysis, we observe that the inferences of all stochastic models are biased towar...
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