Asymmetrical Bi-RNN for pedestrian trajectory encoding

• URL: http://arxiv.org/abs/2106.04419v1
• Date: Tue, 1 Jun 2021 12:05:15 GMT
• Title: Asymmetrical Bi-RNN for pedestrian trajectory encoding
• Authors: Rapha\"el Rozenberg, Joseph Gesnouin and Fabien Moutarde
• Abstract summary: We present a non-symmetrical bidirectional recurrent neural network architecture called U-RNN as a sequence encoder. Experimental results on the Trajnet++ benchmark show that the U-LSTM variant can yield better results regarding every available metric.
• Score: 2.1485350418225244
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Pedestrian motion behavior involves a combination of individual goals and social interactions with other agents. In this article, we present a non-symmetrical bidirectional recurrent neural network architecture called U-RNN as a sequence encoder and evaluate its relevance to replace LSTMs for various forecasting models. Experimental results on the Trajnet++ benchmark show that the U-LSTM variant can yield better results regarding every available metric (ADE, FDE, Collision rate) than common LSTMs sequence encoders for a variety of approaches and interaction modules. Our implementation of the asymmetrical Bi-RNNs for the Trajnet++ benchmark is available at: github.com/JosephGesnouin/Asymmetrical-Bi-RNNs-to-encode-pedestrian-trajectories

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