loading
Documents

Research.Publish.Connect.

Paper

Authors: Hiroaki Minoura ; Tsubasa Hirakawa ; Takayoshi Yamashita and Hironobu Fujiyoshi

Affiliation: Chubu Univeysity, Kasugai, Aichi, Japan

ISBN: 978-989-758-354-4

Keyword(s): Convolutional Neural Network, Long Short-Term Memory, Path Prediction.

Abstract: Path prediction methods with deep learning architectures take into account the interaction of pedestrians and the features of the physical environment in the surrounding area. These methods, however, process all prediction targets as a unified category and it becomes difficult to predict a path suitable for each category. In real scenes, it is necessary to consider not only pedestrians but also automobiles and bicycles. It is considered possible to predict the path corresponding to the type of target by considering the types of multiple targets. Therefore, aiming to achieve path prediction in accordance with individual categories, we propose a path prediction method that represents the target type as an attribute and simultaneously considers the physical environment information. The proposed method inputs feature vectors in a long short-term memory that represents i ) past object trajectory, ii) the attribute, and iii) the semantics of the surrounding area. This makes it possible to p redict a path that is proper for each target. Experimental results show that our approach can predict a path with higher precision. Also, changes in accuracy were analyzed by introducing the attribute of the prediction target and the physical environment information. (More)

PDF ImageFull Text

Download
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 35.172.100.232

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:
Minoura, H.; Hirakawa, T.; Yamashita, T. and Fujiyoshi, H. (2019). Path Predictions using Object Attributes and Semantic Environment.In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, ISBN 978-989-758-354-4, pages 19-26. DOI: 10.5220/0007297500190026

@conference{visapp19,
author={Hiroaki Minoura. and Tsubasa Hirakawa. and Takayoshi Yamashita. and Hironobu Fujiyoshi.},
title={Path Predictions using Object Attributes and Semantic Environment},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,},
year={2019},
pages={19-26},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007297500190026},
isbn={978-989-758-354-4},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,
TI - Path Predictions using Object Attributes and Semantic Environment
SN - 978-989-758-354-4
AU - Minoura, H.
AU - Hirakawa, T.
AU - Yamashita, T.
AU - Fujiyoshi, H.
PY - 2019
SP - 19
EP - 26
DO - 10.5220/0007297500190026

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.