Vehicle target detection methods based on color fusion deformable part model
Zhang EURASIP Journal on Wireless Communications and Networking
Vehicle target detection methods based on color fusion deformable part model
Dongbing Zhang 0
0 School of Computer Science and Technology/Information College, Huaibei Normal University , Huaibei 235000 , China
In this paper, the traditional vehicle target detection method is improved, and a vehicle target detection method based on color fusion deformable part model (DPM) is proposed. Firstly, the traffic image is conducted with HSI color space conversion, and then, the information of each channel in color space is extracted, the DPM of each channel is trained, and then the color fusion DPM is obtained by using the adaptive fusion method. In the process of vehicle detection, traversal search of vehicle images is conducted using color fusion DPM through the sliding window traversal; areas with a score exceeding the threshold are deemed as the vehicle targets. In the experimental phase, we first trained the color fusion DPM, then validated the validity and accuracy, and the experiment images were from the practical traffic junctions. The results show that the method proposed in this paper can carry out vehicle target detection accurately and effectively. Compared with other vehicle target detection methods, it has high detection rate and low false positive rate, which can achieve the accurate detection of vehicle targets in intelligent transportation.
Color fusion DPM; Vehicle detection; HSI; Intelligent transportation
1 Introduction
With the rapid development of intelligent transportation
system, vehicle target detection has become a popular
research field as it is an important part of modern
intelligent transportation system. The traditional vehicle
detection method is to install the induction coil on the
road to collect the vehicle images. The disadvantage of
this method is that the road is damaged and the
installation and maintenance of the system are inconvenient.
With the development of image processing technology,
more and more vehicle detection algorithms based on
computer vision and image processing technology have
been widely used [
1
]. At present, the detection of vehicle
target is based on the existence of motion information,
which is classified into two categories. One is vehicle
detection using motion information, such as inter-frame
difference method [
2
], background difference method
[
3
], and optical flow method [
4, 5
]. These typical
methods rely on the vehicle’s motion information and
the detection algorithm fails when the information is
lost. The other is vehicle detection that does not rely on
motion information, which usually starts with the
characteristics of the vehicle itself. According to the vehicle
shape and posture, many scholars have proposed a
method based on modeling and template matching [
6,
7
]. This kind of method usually builds the vehicle model
first and then matches the model with the test image to
get the vehicle target. This method has high
requirements on the model and is susceptible to noise when the
model is not consistent with the actual situation.
According to the appearance characteristics of the vehicle
such as color and texture, many scholars have proposed
a method based on these features [
8, 9
]. This kind of
method usually studies the appearance difference of
vehicle and non-vehicle in color, texture, etc. This method
allows for better detection of typical vehicle targets but
can easily undetect vehicles that are close to ground
color and texture. Relying on new technologies such as
big data, some scholars have proposed methods based
on statistics and machine learning [
10–12
]. This kind of
methods adopt the idea of statistical analysis, extract the
features suitable for vehicle detection, and adopt neural
network and support vector machine and other methods
to carry out learning and testing. The methods feature a
high accuracy but require a large number of samples for
classifier learning; performance also differs among
different classifiers, and a large amount of computation is
needed. These methods have played a crucial role in
promoting vehicle target detection. Based on the above
methods, this paper proposes a vehicle target detection
method based on color fusion DPM, which takes into
account the vehicle’s gradient information and color
information while modeling the vehicle, and can effectively
and accurately realize the vehicle target detection.
2 Vehicle detection method based on color fusion DPM
2.1 DPM detection principle
Deformable part model (DPM) [
13–15
] is a target
detection classification method featuring high efficiency and
high precision. DPM first establishes a model for the
detection target; the model is divided into a root model and
a part model to make the target shape and posture more
robust. The root model describes the overall
characteristics of the target, and the part model describes the local
detail features of the target; the relationship (...truncated)