Parking detection system using background subtraction and HSV color segmentation

Bulletin of Electrical Engineering and Informatics
Vol. 10, No. 6, December 2021, pp. 3211~3219
ISSN: 2302-9285, DOI: 10.11591/eei.v10i6.3251 3211
Journal homepage: http://beei.org
Parking detection system using background subtraction and
HSV color segmentation
Awang Hendrianto Pratomo, Wilis Kaswidjanti, Alek Setiyo Nugroho, Shoffan Saifullah
Department of Informatics, Universitas Pembangunan Nasional Veteran Yogyakarta, Indonesia
Article Info ABSTRACT
Article history:
Received Aug 31, 2021
Revised Oct 31, 2021
Accepted Nov 2, 2021
Manual system vehicle parking makes finding vacant parking lots difficult, so
it has to check directly to the vacant space. If many people do parking, then
the time needed for it is very much or requires many people to handle it. This
research develops a real-time parking system to detect parking. The system is
designed using the HSV color segmentation method in determining the
background image. In addition, the detection process uses the background
subtraction method. Applying these two methods requires image
preprocessing using several methods such as grayscaling, blurring (low-pass
filter). In addition, it is followed by a thresholding and filtering process to get
the best image in the detection process. In the process, there is a determination
of the ROI to determine the focus area of the object identified as empty
parking. The parking detection process produces the best average accuracy of
95.76%. The minimum threshold value of 255 pixels is 0.4. This value is the
best value from 33 test data in several criteria, such as the time of capture,
composition and color of the vehicle, the shape of the shadow of the object’s
environment, and the intensity of light. This parking detection system can be
implemented in real-time to determine the position of an empty place.
Keywords:
Background subtraction
HSV
Image segmentation
Parking detection system
Smart parking
This is an open access article under the CC BY-SA license.
Corresponding Author:
Wilis Kaswidjanti
Department of Informatics
Universitas Pembangunan Nasional Veteran Yogyakarta
Jl Babarsari 2 Yogykarta, 55281, Indonesia
Email: wilisk@upnyk.ac.id
1. INTRODUCTION
Parking space is a facility that must be fulfilled [1] both in business, shopping, public places, and
institutions. Parking facilities play an essential role in supporting the progress of the place [2]. Thus, a well-
organized parking system makes it comfortable for motorists. Currently, many conventional parking systems
cause parking users to find it difficult to find empty parking spaces. The reason is due to the lack of
information on the parking lot. The extent of the parking area and the number of parked vehicles cause
parking users to have to circle the parking lot first to find an empty parking space. Thus, this system is
inefficient because it can take more time and cost [3]. On these problems, the research develops a real-time
parking detection system which will be developed into a smart parking system [4].
The smart parking system was developed for parking management and monitoring. The current
development uses a sensor-based and image-based approach [5]. Sensor-based monitoring uses infrared to
detect the availability of parking lots. This sensor requires a high cost to implement. In addition, the sensor
can only detect one parking lot, so it requires many sensors to detect several parking lots simultaneously.
Compared with image processing with computer vision, it can detect objects with a broader range than
infrared. This concept uses the camera as an input device to detect the object.

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Bulletin of Electr Eng & Inf, Vol. 10, No. 6, December 2021 : 3211 – 3219
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Based on the concept of computer vision in object detection, this method aims to visualize and
analyze images acquired from cameras using digital image processing techniques [6]. One of the acquisition
processes is using a closed-circuit television (CCTV) camera, which produces digital images in image
processing. The process of detecting the availability of parking lots with CCTV can be done with a marker
[7]. Markers are placed in parking lots, where the camera can detect signs that parking spaces are considered
vacant. The parking lot is considered occupied if the camera does not detect a marker. In the detection
process, bright enough light rays can bounce off the camera to detect the motif. This parking detection
process has been carried out using the canny detection method [8] and dilation [9]. Both methods are
implemented to determine the vacant parking lot [10]. However, the Canny edge detection method relies on
the parking lot boundary line, so the camera must be directed to get the image at the right angle. Meanwhile,
the dilation method still has errors in detecting parking spaces, especially when the parking lot is between
two parked cars.
Dependence detection on specific objects such as markers and parking dividing lines can be solved
using the background subtraction method [11]. The background subtraction method compares two camera-
captured images to detect and track vehicles in the parking area [12]. The background subtraction method is
susceptible to changes in light [13], [14]. So in the process, the method uses the concept of reduction by
sampling background images under certain conditions, such as morning, afternoon, evening, and night. The
background image sampling uses the image energy variable to get a background image that matches the
foreground. However, under certain conditions, the detection results are wrong. The detection error is caused
by the shadow of the object’s environment [15], which is detected as a vehicle. This condition causes the
accuracy to be less than optimal. The shadow effect can be corrected by recognizing the shadow condition of
the object from the foreground image [16], which selects the image background to increase accuracy.
Color image segmentation [17] is our proposal to improve image quality from shadow effects. This
method uses the hue, saturation, value (HSV) color of the shadow. HSV can segment images based on color
by utilizing the upper and lower values of the HSV value so that objects and backgrounds are separated and
can segment noisy color images. HSV accuracy is also better in segmenting color images than red, green, dan
blue (RGB), HSL, and L*A*B [18]. This study aims to provide a solution in real-time parking detection
using a combination of background subtraction HSV segmentation methods to determine the background
image.
In this article, there are four main sections. The first section describes the background, novelty of
the study, and objectives. Next, it presents the methods used in image processing-based parking detection in
the second part. The third part conveys the results and discussion of the process and system testing that has
been designed. Finally, the fifth section presents conclusions on the results obtained and their tests.
2. RESEARCH METHOD
This study uses quantitative methods by testing the effect of a variable in the study. In general, the
stages are shown in Figure 1. This study uses the concept of image processing to detect empty parking lots.
The methods used include searching for image background, preprocessing, background subtraction, and
filtering.
Figure 1. Steps of parking detection system on image processing
2.1. Datasets
The datasets of this study used images acquired by CCTV from the parking lot of the
Communication and Information Office of Gunungkidul Regency. The image has a background that contains
partial or full shadows. The shadow part of the image constantly changes due to changes in the direction of

Bulletin of Electr Eng & Inf ISSN: 2302-9285 
Parking detection system using background subtraction and HSV color … (Awang Hendrianto Pratomo)
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light hitting the object (e.g., buildings, trees, or other objects). In comparison, the full shadow has a similar
shape to the shadow. Based on direct observation, the shadow is due to the presence of sunlight blocked by
clouds. Therefore, we divide the images between 8.00-16.00 and are acquired once every 15 minutes. It aims
so that the image’s shape with a partial shadow does not have a shadow shape. However, this does not apply
to images with full shadows (shadow shapes resemble objects at any given time). The data were tested using
foreground image data with different capture times, vehicle composition and color, the shape of the object’s
environmental shadow, and different light intensities.
2.2. Search for candidate image background
The background image candidate search focuses on image processing to determine the candidate
background image dataset used based on the average number of pixels of 255 per image. The stages of
searching for candidate background images as:
a. Background image is an acquired image that contains two types of data, namely images with partial and
full shadows. The image data used are 20 image samples at each time frame.
b. The image is converted from an RGB image to an HSV image, with three elements: hue representing color,
saturation representing color intensity level, and value representing brightness level. The HSV method also
has an excellent ability to segment color images that have noise. HSV’s accuracy is better in segmenting
images based on color than RGB, HSL, and L*A*B [18]. HSV calculation can be done using (1).
𝐶𝑚𝑎𝑥 = max(𝑅, 𝐺, 𝐵) , 𝐶𝑚𝑖𝑛 = min⁡(𝑅, 𝐺, 𝐵)
(1)
𝐶 = 𝐶𝑚𝑎𝑥 − 𝐶𝑚𝑖𝑛
After the min and max values for each color are obtained, the values for each HSV element can be
calculated using (2). This calculation uses the reference of (1).
H =
{
60 (
𝐺 − 𝐵
𝐶
) , 𝑖𝑓⁡𝐶𝑚𝑎𝑥 = 𝑅
120 + 60 (
𝐵 − 𝑅
𝐶
) , 𝑖𝑓⁡𝐶𝑚𝑎𝑥 = 𝐺
240 + 60 (
𝑅 − 𝐺
𝐶
) , 𝑖𝑓⁡𝐶𝑚𝑎𝑥 = 𝐵
(2)
𝑆 = {
0, 𝐶𝑚𝑎𝑥 = 0
𝐶
𝐶𝑚𝑎𝑥
⁡, 𝐶𝑚𝑎𝑥 ≠ 0
𝑉 = 𝐶𝑚𝑎𝑥
c. HSV can segment color images by utilizing the upper and lower values of the HSV value to separate
objects from the background. This value can be calculated by trial and error and produces a lower value
(0, 0, 0) and an upper value (179, 255, 85).
d. Noise reduction uses the median filter method to reduce noise better than the same linear smoothing
model [19]. The following is the equation for the median filter equation.
𝑥 =
𝑛+1
2
(3)
e. After the median filter process, the next step is to find the number of pixels of 255 (white color) per
existing frame.
f. Find an average of 255 pixels for each image frame.
𝐴𝑣𝑒𝑟𝑎𝑔𝑒 =
𝑡𝑜𝑡𝑎𝑙⁡𝑜𝑓⁡255⁡𝑝𝑖𝑥𝑒𝑙⁡𝑒𝑣𝑒𝑟𝑦⁡𝑓𝑟𝑎𝑚𝑒
𝑡𝑜𝑡𝑎𝑙⁡𝑜𝑓⁡𝑓𝑟𝑎𝑚𝑒
(4)
g. The average result becomes a reference for finding candidate background images.

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2.3. Preprocessing
Preprocessing is used to get the selected background image from the existing candidate background
image with the shadow condition closest to the foreground image (real-time) [20]. In addition, this process
aims to minimize shadow noise from the object’s environment. The preprocessing stages carried out in this
study include as:
a. The candidate background image and foreground image (real-time) are converted to the HSV color space,
then segmented by shadow color and filter median as in the previous step.
b. The stage of determining the background image uses the slightest 255-pixel difference between the
candidate background and foreground images.
c. The candidate background image that has the slightest 255-pixel difference becomes the selected
background image.
2.4. Background subtraction
The selected background image becomes a real-time reference for performing background subtraction
between the background and foreground images. The stages of background subtraction include as:
a. Grayscale is a digital image that has only one channel value per pixel. This value indicates the level of
color intensity ranging from black, gray, and white. This image in each pixel has a color from white to
black, where each pixel is represented by 8 bits. The grayscale process can be done using (5) [21], [22].
𝑌(𝑥,𝑦) = (0,229 × 𝑅) + (0,587 × 𝐺) + (0,114 × 𝐵) (5)
b. Blurring is a low-pass filter that takes an image with a smooth, intense gradation. High-intensity differences
are reduced or removed to reduce noise in an image. The blurring process itself aims to change the intensity
of the noise image to gray so that when the thresholding process is carried out, the noise image will be
reduced. The gaussian distribution equation can be seen in the following equation [23].
𝐺(𝑥, 𝑦) =⁡
1
2𝜋𝜎2 ⁡𝑒
−⁡
𝑥2+𝑦2
2𝜎2
𝐺(𝑥, 𝑦) =⁡
1
2𝜋𝜎2 ⁡𝑒
−⁡
𝑥2+𝑦2
2𝜎2
⁡ =⁡
1
𝜎√2𝜋
⁡𝑒
−⁡
𝑥2
2𝜎2 1
𝜎√2𝜋
⁡𝑒
−⁡
𝑦2
2𝜎2
= 𝐺(𝑥)𝐺(𝑦) (6)
c. Background subtraction is one method of the segmentation process that separates moving objects
(foreground) and background objects (background) [24]. Background subtraction has a vital role in
computer vision, one of which is monitoring systems. The role played in background subtraction is
knowing or distinguishing between the background and objects in an image. The following is the
background subtraction (7):
𝑅𝑘(𝑥, 𝑦) = |𝑓𝑘(𝑥, 𝑦) − 𝐵𝑘(𝑥, 𝑦)|
𝐷𝑘(𝑥, 𝑦) = {
255⁡, 𝑏𝑎𝑐𝑘𝑔𝑟𝑜𝑢𝑛𝑑⁡𝑅𝑘(𝑥, 𝑦) > 𝑇
0⁡, 𝑏𝑎𝑐𝑘𝑔𝑟𝑜𝑢𝑛𝑑⁡𝑅𝑘(𝑥, 𝑦) ≤ 𝑇
(7)
Background subtraction on the foreground image (real-time image) and the selected background image,
each of which has been blurred in the previous step. The purpose of segmentation [25] using background
subtraction is to obtain objects from the resulting difference between the foreground and the selected
background images. The results of background subtraction can be seen in Figure 2.
(a) (b) (c)
Figure 2. The results of background subtraction with, (a) foreground image, (b) background image, (c) difference

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d. Thresholding is used to filter the noise contained in an image. Thresholding is the process of converting a
grayscale image into a binary or black and white image so that it can be seen between the foreground and
background in an image, separating the pixel values according to a predetermined threshold. Thresholding
can be written as 8.
𝐺(𝑥,𝑦) = {
⁡𝑖𝑠⁡255⁡𝑖𝑓⁡𝐹(𝑥,𝑦)) > 𝑇
𝑖𝑠⁡0⁡𝑖𝑓⁡𝐹(𝑥,𝑦)) ≤ 𝑇
(8)
The following is the result of the background subtraction stage after the threshold is carried out, as
shown in Figure 3.
Figure 3. Sample of the results image of thresholding
2.5. Filtering
Filtering is a process to take or remove specific frequencies from an image [26]. At this stage, it is
done to reduce or minimize the existing noise. It was starting from doing blurring and thresholding again. It
was then continued with closing morphology and median filter.
a. The blurring is done again because the image produced in the previous stage is still too rough.
b. Threshold is done again to emphasize the results of blurring that is done.
c. The results of the background subtraction method show that there is still noise in the image results, then
to remove the noise, the researcher uses morphological operations with the opening method. The closing
morphology is the opposite of the opening morphology. In the closing morphology, dilation is carried out
first and then followed by erosion. Dilation enlarges the binary image by adding layers around the object,
while erosion is the opposite, reducing or eroding the object’s edges.
d. In the image of the closing morphological results, there is still noise in the form of object spots in the
image. Therefore, to reduce the noise, the researchers used the median filter method. The median filter
method takes a specific area of the image according to predetermined kernel size. The filtered image can
be seen in Figure 4.
Figure 4. Filtering result on the parking area
2.6. Detection and classification
The detection process is a process to get an object in the form of 255 pixels. The detection stages as:
a. Region of interest (ROI) works in coding differently in some regions of the digital image. The more
critical image area will have a better image quality than the surrounding area. Thus, ROI can be used to
limit the area of a vehicle. Therefore, a ROI formed a focus area for detecting pixel 255 in each parking
slot available in the parking lot. The following is an example of ROI, as shown in Figure 5.
b. The number of 255 pixels contained in each ROI is the result of the resulting detection.

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Classification is carried out based on the minimum percentage of objects in the form of white pixels
(255) to the total pixels in each area. If an area has a pixel percentage of 255 less than the minimum
percentage limit, then the parking lot is considered empty. At the same time, an area with an object
percentage is more than the minimum limit, and then the parking lot is considered occupied or filled.
Figure 5. ROI
3. RESULTS AND DISCUSSION
The result of this research is the detection of empty parking spaces. The final result was tested to
determine the level of accuracy resulting from the background subtraction method and the hue saturation
value for parking detection. Accuracy testing is done by observing the detection of parking lots using the
method used with the actual data obtained. The test data consisted of 33 foreground images obtained from
random CCTV camera captures during sunny conditions from 8.00 to 16.00. The foreground image has
different characteristics from the time of image capture, the shape of the shadow contained in the image, the
intensity of light, and the composition and color of the vehicle parked in it (number and type of car).
The level of accuracy is calculated using the confusion matrix, namely by comparing the
classification results from parking detection results from the introduction of the system with the actual
parking slots. The classification uses a different minimum threshold value of 255 pixels to get the best
accuracy value. The minimum threshold value of 255 pixels used to classify the test data used is between 0 to
1, with a difference of 0.1.
The accuracy test with the confusion matrix has four terms to represent the results of the
classification process, namely true positive (TP), true negative (TN), false positive (FP), and false negative
(FN) [27]. The TP value is obtained if the number of parking slots filled with vehicles is detected correctly by
the system, and the TN value is obtained if the number of available (empty) parking slots is detected
correctly by the system. The FP value is obtained if the number of parking slots filled with vehicles is
detected incorrectly by the system. The FN value is obtained if the number of parking slots filled with
vehicles is detected incorrectly by the system. An available (empty) parking slot was detected incorrectly by
the system. An example of testing on one of the test data is shown in Figure 6 (a). The third test data has
contained in 12 parking slot images. Figure 6 is one of the test data used for this study. In the test image, the
coordinates of each existing parking slot are determined first. The coordinates of the parking slots are used as
a ROI after the image is processed. The image processing results are shown in Figure 6 (b), which is not yet
clear for the desired slot. So, this process needs to be transformed to get more apparent results.
(a) (b)
Figure 6. These figures are, (a) sample of the third testing data, (b) the result of image processing on the third
testing data
The transformation process uses a region of interest concept with a perspective transformation at a
predetermined coordinate point. The height of the perspective image in this study is 200 px, and the width is
120 px. The establishment of a region of interest aims to focus the detection process on a certain point. The
identification of ROI images based on these images is produced in Figure 7, starting from ROI 0 to ROI 9.
This ROI shows that each part is a reference for the empty parking detection process. ROI that detects 255

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pixels will be classified as a parking lot. The processed ROI has different threshold values and classifications
based on the calculation, as shown in Table 1. In Table 1, an example of the implementation of ROI from the
third image data processed with ten total ROI and the variation of the threshold value between 0 and 1 with a
difference of 0.1 shows different results.
Figure 7. Results of the region of interest
Table 1. The results of classification on the third testing data
ROI Pixel 255 Pixel ROI (Area) Value
Threshold
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
ROI 0 27096 32000 0.84675         ✓ ✓
ROI 1 24860 32000 0.776875        ✓ ✓ ✓
ROI 2 20781 32000 0.64940625       ✓ ✓ ✓ ✓
ROI 3 26730 32000 0.8353125         ✓ ✓
ROI 4 29555 32000 0.92359375          ✓
ROI 5 5669 32000 0.17715625  ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
ROI 6 714 32000 0.0223125 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
ROI 7 16234 32000 0.5073125      ✓ ✓ ✓ ✓ ✓
ROI 8 1883 32000 0.05884375 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
ROI 9 3800 32000 0.11875  ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
Note: =parking slots filled; =parking slots available
The classification process was tested using the confusion matrix method. An example of accuracy
testing is shown in Figure 8. Figure 8 has three kinds of detection because it uses different thresholds. The
thresholds used are 0.1, 0.4, and 0.8. The the accuracy calculation of this experiment can be seen in Table 2.
Based on the tests in Table 2, the third test data with a threshold of 0.1 has an accuracy of 80%. This
first test has a value of FN=2 because there is an empty parking slot but identifies the presence of a vehicle. It
can be seen from the color of the red box, which identifies that the parking lot has been filled. Two empty slots
were boxed in red from the experiment, caused by the light covering/blocking the parking lot. Thud, the
following experiment is to increase the threshold value to 0.4; when this threshold is implemented, the accuracy
increases to 100%, where the condition of all parking slots is classified correctly.
(a) (b) (c)
Figure 8. Detection results on the third testing data using threshold, (a) 0.1, (b) 0.4, (c) 0.8
Table 2. The accuracy of testing data based on Figure 8
Figure
Threshold
Accuracy (%)
TP TN FP FN
9.(a) 6 2 0 2 80
9.(b) 6 4 0 0 100
9.(c) 3 4 3 0 70
In addition, the third test is to use a more significant threshold of 0.8. This experiment yielded an
accuracy of 70%. The test has a value of FP=3. This situation is because the parking slot is filled with

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vehicles but is considered empty by the system. The slot that is considered empty is probably because the
detection of the image used shows that the object image is close to the background image. At the end of the
accuracy testing process, each threshold value in the overall test data is summed and averaged. These results
are used to obtain the maximum accuracy value. The average accuracy results for each test with various
threshold values are shown in Figure 9.
Figure 9. Detection results on the third testing data using threshold=0.1
Based on Figure 9, the average accuracy of all tested data is at a different minimum threshold value
of 255 pixels. The highest accuracy result is 95.76%, with a minimum threshold value of 0.4. At the same
time, the lowest accuracy is 30.06%, with a minimum threshold value of 1. The detection results can provide
the best accuracy for classifying empty parking slots.
4. CONCLUSION
The background subtraction method can be implemented in determining the image background. The
addition of the HSV segmentation method in determining the background provides the best detection results
with an average accuracy of 95.76%. This testing process is based on a minimum threshold value of 0.4 at
255 pixels. This threshold has the best resistance from 33 test data based on several factors: the time of
capture, vehicle composition and color, the shape of the object’s environmental shadow, and different light
intensities. However, the detection process is still very dependent on the position and angle of the CCTV
camera to the parking lot. This condition results in the possibility of errors still occurring at certain parking
positions. This error occurs because the system detects vehicles in the parking lot next to the detected object
is considered an incoming object, and objects other than vehicles contained in the parking lot are still
considered occupied parking lots. In the future, to improve this, it is necessary to apply a machine vision
method that detects objects according to the type of vehicle. It also needed to set the equipment of
image/video acquisition to obtain a specific object.
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