Pedestrian Lane Detection for Assistive Navigation of Vision-Impaired People: Survey and Experimental Evaluation

Author:

Lei Yunjia1ORCID,Phung Son Lam1ORCID,Bouzerdoum Abdesselam1ORCID,Thanh Le Hoang1ORCID,Luu Khoa2ORCID

Affiliation:

1. School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW, Australia

2. Computer Science and Computer Engineering Department, University of Arkansas, Fayetteville, AR, USA

Funder

Discovery Project

titled “Assistive Micro-navigation for Vision Impaired People” from the Australian Research Council, and a matching Ph.D. scholarship from the University of Wollongong

Queensland Department of Transport and Main Roads

New SouthWales (NSW) Space Research Network

NSW Defence Innovation Network

NSW Government

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Reference75 articles.

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. MSSP: A Multi-view Benchmark for Street Scene Perception in Assistive Navigation;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

2. Efficient Lane Detection Method for Challenging Road Environments;2024 2nd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA);2024-03-15

3. Remote Virtual Companion via Tactile Codes and Voices for The People With Visual Impairment;PROG BIOCHEM BIOPHYS;2024

4. MSD-NAS: multi-scale dense neural architecture search for real-time pedestrian lane detection;Applied Intelligence;2023-08-11

5. SPARTAN: Self-supervised Spatiotemporal Transformers Approach to Group Activity Recognition;2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW);2023-06

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