loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Mostafa Waleed and Amr El Mougy

Affiliation: German University in Cairo, Cairo, Egypt

Keyword(s): Intention Prediction, Autonomous Vehicles, Advanced Driver Assistance System.

Abstract: Accurate prediction of the intention of pedestrians to cross the path of vehicles is highly important to ensure their safety. The accuracy of these intention prediction systems is dependent on the recognition of several pedestrian-related features such as body pose, head pose, pedestrian speed, and passing direction, as well as accurate analysis of the developing traffic situation. Previous research efforts often focus only on a subset of these features, therefore producing inaccurate or incomplete results. Accordingly, this paper presents a comprehensive model for pedestrian intention prediction that incorporates the recognition of all the above features. We also adopt the Constant Velocity Model to estimate the future positions of pedestrians as early as possible. Our model includes a reasoning engine that produces a decision based on the output of the recognition systems of all the aforementioned features. We also consider occlusion scenarios that happen when multiple pedestrians are crossing simultaneously from the same or different directions. Our model is tested on well-known datasets as well as a real autonomous vehicle, and the results show high accuracy in predicting the intention of pedestrians in different scenarios, including ones with occlusion among pedestrians. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.191.200.35

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Waleed, M. and El Mougy, A. (2021). Multi-feature and Modular Pedestrian Intention Prediction using a Monocular Camera. In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-484-8; ISSN 2184-433X, SciTePress, pages 1160-1167. DOI: 10.5220/0010337711601167

@conference{icaart21,
author={Mostafa Waleed. and Amr {El Mougy}.},
title={Multi-feature and Modular Pedestrian Intention Prediction using a Monocular Camera},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2021},
pages={1160-1167},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010337711601167},
isbn={978-989-758-484-8},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Multi-feature and Modular Pedestrian Intention Prediction using a Monocular Camera
SN - 978-989-758-484-8
IS - 2184-433X
AU - Waleed, M.
AU - El Mougy, A.
PY - 2021
SP - 1160
EP - 1167
DO - 10.5220/0010337711601167
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic