Assessing the performance of systems for far
and near infrared detection of pedestrians
Aisikaer Wubulikasimu
This thesis is part of an EU project that aims to create a warning system against collisions
with pedestrians during night driving. Two image analysis systems, respectively based on
Near Infrared (NIR) and Far-Infrared (FIR) technologies, are being fused, and the
integrated system is used to assess the presence or absence of pedestrians ahead of the
vehicle. Here, we propose a general performance measure for warning systems based on
computer analysis of digital images. The system detections are compared to a ground
truth obtained by visual inspection of the recorded digital images, and a similarity
measure is developed to quantify how well the computerized systems can determine the
number of pedestrians and their positions ahead of the vehicle. More specifically, we
modify a metric originally proposed by Hausdorff to measure distances between sets of
points in any metric space. Thereafter, we examine how well two widely used numerical
classification techniques can discriminate between images with and without pedestrians.
The two investigated classification techniques are based on Singular Value
Decomposition (SVD) and Non-negative Matrix Factorization (NFM), respectively.
Applications to synthetic and real digital images showed that our performance measure
was relevant and easy to comprehend. The numerical algorithms for detecting pedestrians
in digital images performed relatively well but more advanced systems are needed to
ensure high accuracy.