Pedestrian Detection in Low-Light Conditions: A Comprehensive Survey
Bahareh Ghari Ali Tourani Asadollah Shahbahrami Georgi Gaydadjiev
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 six percent of the works.