Pedestrian detection in low-light conditions: A comprehensive survey

Autonomous vehicles; Computer vision; Object detection; Pedestrian detection; Autonomous driving; Objects detection; Signal Processing; Computer Vision and Pattern Recognition; Computer Science - Computer Vision and Pattern Recognition

Abstract :

[en] Pedestrian detection remains a critical problem in various domains, such as computer vision, surveillance, and autonomous driving. In particular, accurate and instant detection of pedestrians in low-light conditions and reduced visibility is of utmost importance for autonomous vehicles to prevent accidents and save lives. This paper aims to comprehensively survey various pedestrian detection approaches, baselines, and datasets that specifically target low-light conditions. The survey discusses the challenges faced in detecting pedestrians at night and explores state-of-the-art methodologies proposed in recent years to address this issue. These methodologies encompass a diverse range, including deep learning-based, feature-based, and hybrid approaches, which have shown promising results in enhancing pedestrian detection performance under challenging lighting conditions. Furthermore, the paper highlights current research directions in the field and identifies potential solutions that merit further investigation by researchers. By thoroughly examining pedestrian detection techniques in low-light conditions, this survey seeks to contribute to the advancement of safer and more reliable autonomous driving systems and other applications related to pedestrian safety. Accordingly, most of the current approaches in the field use deep learning-based image fusion methodologies (i.e., early, halfway, and late fusion) for accurate and reliable pedestrian detection. Moreover, the majority of the works in the field (approximately 48%) have been evaluated on the KAIST dataset, while the real-world video feeds recorded by authors have been used in less than 6 % of the works.

Research center :

Interdisciplinary Centre for Security, Reliability and Trust (SnT) > ARG - Automation & Robotics

Disciplines :

Computer science

Author, co-author :

Ghari, Bahareh; Department of Computer Engineering, University of Guilan, Rasht, Iran

TOURANI, Ali ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Automation

Shahbahrami, Asadollah; Department of Computer Engineering, University of Guilan, Rasht, Iran

Gaydadjiev, Georgi; Department of Quantum and Computer Engineering, Delft University of Technology, Delft, Netherlands

External co-authors :

yes

Language :

English

Title :

Pedestrian detection in low-light conditions: A comprehensive survey

Publication date :

August 2024

Journal title :

Image and Vision Computing

ISSN :

0262-8856

Publisher :

Elsevier Ltd

Volume :

148

Pages :

105106

Peer reviewed :

Peer Reviewed verified by ORBi

Focus Area :

Computational Sciences

Development Goals :

9. Industry, innovation and infrastructure

Additional URL :

https://www.sciencedirect.com/science/article/pii/S0262885624002105?via%3Dihub

Commentary :

23 pages, 3 tables, 10 figures

Available on ORBilu :

since 17 July 2024

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