Original scientific paper

https://doi.org/10.17559/TV-20210311111839

Generator with Triangulation for Pedestrians Trajectory Prediction

Xiuhong Ma ; Hebei University of Economics and Business School of Management Science and Engineering, Hebei University of Economics and Business, Shijiazhuang, 050061, China
Haitao Wang* ; Hebei University of Economics and Business Modern Educational Technology Center, Hebei University of Economics and Business, Shijiazhuang, 050061, China
Qiulin Ma ; Beijing Jiaotong University Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, 100044, China

Abstract

Pedestrian trajectory prediction is a basic task in computer vision field. The prosperity of artificial intelligence makes the automatic drive, human-robot interaction and surveillance video attract a great deal of attention. Generally, researchers always place emphasis on pedestrian trajectory. The focuses of pedestrian trajectory prediction task are motion pattern modelling and spatio-temporal interaction modelling in the current study. In our paper, we present a GAN-based framework to model pedestrian motion pattern. A Delaunay triangulation algorithm is applied to map the pedestrian interaction. From the perspective of space, both the position interaction and motion interaction of pedestrians can be considered. For example, the influence of the movement direction and motion potential energy of pedestrians on the surrounding pedestrians can be modelled.

Keywords

delaunay triangulation; generative adversarial nets; pedestrian trajectory prediction

Hrčak ID:

260851

URI

https://hrcak.srce.hr/260851

Publication date:

22.7.2021.

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