Detection of cars in complex urban areas
File(s)
Accepted version
Author(s)
ElMikaty, M
Stathaki, P
Type
Conference Paper
Abstract
Detection of cars in airborne images of typical urban
areas has various applications in several domains,
such as surveillance, military and remote sensing. It
is a tremendously-challenging problem, mainly because
of the significant inter-class similarity among various
objects in urban environments. In this paper, a novel
framework is introduced that adopts a sliding-window
approach and it depicts, in a novel way, the local distribution
of gradients, colours and texture. A linear support
vector machine classifier is used to differentiate
between descriptors that belong to cars and descriptors
that belong to other objects in a hyperspace of 3838
dimensions. Descriptors are computed over a newlyproposed
adaptive distribution of cells that enables the
use of various rotation-variant image descriptors. The
proposed framework has been evaluated on the Vaihingen
dataset and results corroborate its superiority as it
achieves a higher precision for a given recall than the
state of the art.
areas has various applications in several domains,
such as surveillance, military and remote sensing. It
is a tremendously-challenging problem, mainly because
of the significant inter-class similarity among various
objects in urban environments. In this paper, a novel
framework is introduced that adopts a sliding-window
approach and it depicts, in a novel way, the local distribution
of gradients, colours and texture. A linear support
vector machine classifier is used to differentiate
between descriptors that belong to cars and descriptors
that belong to other objects in a hyperspace of 3838
dimensions. Descriptors are computed over a newlyproposed
adaptive distribution of cells that enables the
use of various rotation-variant image descriptors. The
proposed framework has been evaluated on the Vaihingen
dataset and results corroborate its superiority as it
achieves a higher precision for a given recall than the
state of the art.
Date Issued
2017-07-20
Date Acceptance
2017-02-13
Citation
Machine Vision Applications (MVA), 2017 Fifteenth IAPR International Conference on, 2017
Publisher
IEEE
Journal / Book Title
Machine Vision Applications (MVA), 2017 Fifteenth IAPR International Conference on
Copyright Statement
© MVA Organization All Rights Reserved
Source
IAPR Conference on Machine Vision Applications
Publication Status
Published
Start Date
2017-05-08
Finish Date
2017-05-12
Coverage Spatial
Nagoya, Japan