Take a NAP: Non-Autoregressive Prediction for Pedestrian Trajectories

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2 Citations (Scopus)


Pedestrian trajectory prediction is a challenging task as there are three properties of human movement behaviors which need to be addressed, namely, the social influence from other pedestrians, the scene constraints, and the multimodal (multi-route) nature of predictions. Although existing methods have explored these key properties, the prediction process of these methods is autoregressive. This means they can only predict future locations sequentially. In this paper, we present NAP, a non-autoregressive method for trajectory prediction. Our method comprises specifically designed feature encoders and a latent variable generator to handle the three properties above. It also has a future-time-agnostic context generator and a future-time-oriented context generator for non-autoregressive prediction. Through extensive experiments that compare NAP against eleven recent methods, we show that NAP achieves state-of-the-art trajectory prediction performance.

Original languageEnglish
Title of host publicationNeural Information Processing - 27th International Conference, ICONIP 2020, Proceedings
EditorsHaiqin Yang, Kitsuchart Pasupa, Andrew Chi-Sing Leung, James T. Kwok, Jonathan H. Chan, Irwin King
PublisherSpringer Science + Business Media
Number of pages13
ISBN (Print)9783030638290
Publication statusPublished - 2020
Event27th International Conference on Neural Information Processing - Bangkok, Thailand
Duration: 18 Nov 202022 Nov 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12532 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference27th International Conference on Neural Information Processing
Abbreviated titleICONIP 2020


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