Pedestrian Trajectory Prediction in Heterogeneous Traffic using Facial Keypoints-based Convolutional Encoder-decoder Network

Document Type

Article

Publication Date

11-14-2022

Abstract

Future pedestrian trajectory prediction offers great prospects for many practical applications such as unmanned vehicles, building evacuation design and robotic path planning. Most existing methods focus on social interaction among pedestrians but ignore the fact that heterogeneous traffic objects (cars, dogs, bicycles, motorcycles, etc.) have significant influence on the future trajectory of a subject pedestrian. Also, the walking direction intention of a pedestrian may be referred by his/her facial keypoints. Considering this, this work proposes to predict a pedestrian's future trajectory by jointly using neighboring heterogeneous traffic information and his/her facial keypoints. To fulfill this, an end-to-end facial keypoints-based convolutional encoder-decoder network (FK-CEN) is designed, in which the heterogeneous traffic and facial keypoints are input. After training, FK-CEN is evaluated on 5 crowded video sequences collected from the public datasets MOT-16 and MOT-17. Experimental results demonstrate that it outperforms state-of-the-art approaches, in terms of prediction errors.

Identifier

85151514364 (Scopus)

Publication Title

ACM Transactions on Internet Technology

External Full Text Location

https://doi.org/10.1145/3410444

e-ISSN

15576051

ISSN

15335399

Issue

4

Volume

22

Grant

61473013

Fund Ref

National Natural Science Foundation of China

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