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Real-time intent prediction of pedestrians for autonomous ground vehicles via spatio-temporal dense net

Version 2 2024-06-04, 02:21
Version 1 2019-09-17, 12:08
conference contribution
posted on 2024-06-04, 02:21 authored by K Saleh, M Hossny, S Nahavandi
© 2019 IEEE. Understanding the behaviors and intentions of humans are one of the main challenges autonomous ground vehicles still faced with. More specifically, when it comes to complex environments such as urban traffic scenes, inferring the intentions and actions of vulnerable road users such as pedestrians become even harder. In this paper, we address the problem of intent action prediction of pedestrians in urban traffic environments using only image sequences from a monocular RGB camera. We propose a real-time framework that can accurately detect, track and predict the intended actions of pedestrians based on a tracking-by-detection technique in conjunction with a novel spatio-temporal DenseNet model. We trained and evaluated our framework based on real data collected from urban traffic environments. Our framework has shown resilient and competitive results in comparison to other baseline approaches. Overall, we achieved an average precision score of 84.76% with real-time performance at 20 FPS.

History

Pagination

9704-9710

Location

Montreal, Quebec

Start date

2019-05-20

End date

2019-05-24

ISSN

1050-4729

ISBN-13

9781538660263

Language

eng

Publication classification

E1 Full written paper - refereed

Title of proceedings

ICRA 2019 : Proceedings of the IEEE International Conference on Robotics and Automation

Event

Robotics and Automation. Conference (2019 : Montreal, Quebec)

Publisher

IEEE

Place of publication

Piscataway, N.J.

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