A System for Real-time On-street Parking Detection
and Visualization on an Edge Device
Akihiro Matsuda1 , Tomokazu Matsui1 , Yuki Matsuda1,2 , Hirohiko Suwa1,2 , Keiichi Yasumoto1,2 ,1Nara Institute of Science and Technology, Ikoma, Nara 630-0192, Japan
E-mail: (matsuda.akihiro.lr2, matsui.tomokazu.mo4, yukimat, h-suwa, yasumoto)@is.naist.jp
2
RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan

Abstract—In recent years, street parking in prohibited areas
has become a serious social problem, particularly in metropolitan
and tourist areas where there are many on-street parked cars. In
addition, because on-street parking can cause traffic congestion
and accidents, real-time detection is considered necessary. In
previous studies, fixed-point cameras have been mainly used for
traffic control; however, a major disadvantage of these systems istheir limited detection area. In this study, we developed a systemthat can detect and visualize on-street parking in real-time using
video data captured by dashboard cameras, which have become
widely used in recent years. We created a learned model to detecton-street parking and to recognize cars using an edge device. By
displaying the location information of on-street parked cars on amap, their location can be visualized. This system could be used
to obtain statistical data for crackdowns on on-street parking
and to identify areas where on-street parking occurs.
Index Terms—street parking, edge device, real-time sensing,dashboard camera, object detection

I. I NTRODUCTION
In recent years, street parking in prohibited areas has
become a serious social problem, especially in metropolitan
and tourist areas. According to a survey on street parking
conducted by the Traffic Bureau of the National Police Agency(Japan) in 2019, the number of on-street parked vehicles inprohibited areas in the special wards of Tokyo was approximately 52,700 [1]. Illegal street parking is not only hazardous,
but it can also cause a variety of other traffic problems, suchas traffic jams and rear-end collisions.
To deal with on-street parking and its related problems, theextension of restricted parking zones and the implementationof time-limited zones are being considered. However, thesemeasures are not a sustainable solution and they may complicate existing problems. In addition, they require large-scalecooperation from the government and administrative agencies,
which is very costly and time-consuming [2]. A previous
study proposed a system that uses multiple fixed-point cameras
installed in the city to detect on-street parking. However, fixedpoint cameras can only cover a limited area.
In this study, we sought to develop a system capable ofdetecting on-street parked cars more efficiently and widely
than fixed-point cameras in real-time using dashboard camera
video systems, which are increasingly being used in generalvehicles [3]. A dashboard camera records every situation in acity and stores an overwhelming amount of information. Also,
it is more efficient than traditional traffic monitoring methods

because it is not limited by area. On the other hand, uploading
all the collected videos to the cloud for analysis is problematicin terms of the loads placed on communications networks,
communication cost, and real-time performance. To reduce
communication costs, we sought to determine whether or not
the vehicle has been parked illegally in real-time using an edge
device that has been installed in the vehicle and only uploads
parking-related information to the cloud. In this study, we also
propose a real-time visualization system for determining thelocation of on-street parked cars using a video-learning modeland a dashboard camera. This information is captured andprocessed on an edge device mounted on a vehicle.
The remainder of this manuscript is organized as follows.
In Section 2, research related to the detection of on-street
parking is reviewed and the problems associated with existing
methods are summarized. Section 3 defines on-street parking
and describes the issue of on-street parking in Japan. Section
4 provides an overview of a system for real-time detection
and visualization of on-street parking. In Section 5, a method
for on-street parking detection on an edge device is described.
In Section 6, we describe the accuracy of instances of onstreet parking by the model, and the speed required forrecognition and processing on the edge device. In Section 7,
a method for visualizing the location information of on-street
parked vehicles is described. Finally, Section 8 presents theconclusions of this thesis and discusses future directions of
this research.
II. R ELATED R ESEARCH
In this section, we review studies and existing technologies
related to on-street parking. Although numerous studies havebeen conducted on on-street parking to date, few have examined how on-street parking can be detected [4] [5] [6].
Wen et al. proposed a system for vehicle recognition,
location detection, and classification using Mobile Laser Scanning (MLS) point clouds [7]. Briefly, their method involved
segmenting the recognition targets into point clouds and thenfitting a vehicle recognition model to each target. The acquired
information, such as position and orientation, was then compared with data acquired at different times to estimate theconsistency of the features with their durations. The system
was highly accurate in recognizing and classifying vehicles
and detecting changes, but recognition was limited by thereach of the MLS and requires scanning through cars parked

Output

Input…On-street parking detected>SpeedPosition information

Feature extraction(only on-street parking)>Acceleration>GPS

Bounding box information

Recording car information

Installed in vehicles

Object Detection

Model Output

Model Input

Mapping of locations where
on-street parking was detected

Street Parking?

Videos taken by dashboard camera

0：Not On-Street Parked Vehicle

Learned Model
1：On-street Parked Vehicle

Fig. 1. Overall configuration of the proposed systemalong the side of the street. Further, real-time recognition isdifficult because of the need for different time data.
Xie et al. proposed a system for detecting illegal parking
based on the Single Shot MultiBox Detector (SSD) algorithm[8]. SSD is used to detect and track vehicles in the region ofinterest and count the number of static objects. However, thissystem uses fixed-point camera images and the detection area
is limited. In addition, it is difficult to judge whether a vehicle
is engaged in on-street parking or waiting at a traffic signal (allstationary vehicles are counted). All of the abovementioned
methods have limitations in terms of detection areas anddetection conditions, making them suboptimal. In practicalapplications, on-street parked vehicles are spread out throughout the city and conditions vary. In this study, we propose amethod for lane- and past-information-independent detectionthat uses video captured by dashboard-mounted cameras.
Specifically, we sought to develop a system that can efficiently and accurately make real-time decisions regarding
on-street parking using an edge device, and to visualize thelocation of on-street parked vehicles to resolve the problems
highlighted in the studies described in this section.
III. S TREET PARKINGIn this section, we discuss on-street parking. Specifically, we
examine the distinguishing characteristics of vehicles engaged
in on-street parking from images captured by a dashboard camera. We then define the common characteristics of on-street
parking. Based on these definitions, we determine whether or
not a vehicle is parked in the street.
A. Street Parking in Japan
On-street parking in prohibited areas is widespread in Japan,
particularly in urban and tourist areas. An example of on-street
parking is shown in Fig2. A vehicle parked on the left side of alane, regardless of the number or width of lanes, is considered
to be engaged in on-street parking. A wide variety of vehicles
engage in on-street parking, and detailed descriptions of suchsituations are described in the following section.

Fig. 2. Street parking in Japan (red frame)

B. Definition of on-street parking
The common characteristic of on-street parking is define asa condition. This definition makes it possible to determine
whether or not a vehicle is engaged in on-street parking.
Further, in the conditions described below, it is assumed thatthe target vehicle is stationary.
1) Determination by brake lights: One of the characteristics
of on-street parking is if the vehicle is stationary on a road andthe vehicle’s brake lights (tail lights) off. Normally, the brakes
of a vehicle need to be applied in order for the vehicle to stopat a traffic light or pause in the road. A manual transmission(MT) car may maintain the vehicle in a stationary position
without applying the brake, but these situations have become
almost non-existent in recent years as automatic (AT) cars now
account for most car sales. Furthermore, from a safety point ofview, stopping without turning the brake lights on is dangerous
because of the risk of rear-end collisions and other problems.
Thus, if a vehicle on the road is stationary and its brake lights
are not on, then that vehicle is considered to be an on-street
parked vehicle.
2) Determination by hazard lights: One of the conditions
that need to be met for on-street parking is if the vehicle isstopped at the side of the road with its hazard lights on. Many
street-parked cars turn on their hazard lights to indicate that

they are parked on the street. Most of these stationary cars
have only their hazard lights on, and their brake lights off.
Although hazard lights are often used in situations other thanthose for which they are intended, many vehicles parked on
the side of the road with their hazard lights on are considered
to be on-street parked vehicles.
3) Determination by lane occupancy: Another condition
that needs to be met for on-street parking is if the lane
occupancy is low, or if the front and back tires of the vehicle
are near or extend beyond the line indicating the outer edgeof the roadway opposite the centerline. This happens because
vehicles park on the street and stop at the side of the road.
Street parking occurs at the left side of the travel lane and the
distance between the vehicle’s tires and the centerline of thelane is typically far. Therefore, for on-street parking, which is
the object of detection for the video on the dashboard camera,
increased attention is given to the area left of center in thevideo. Therefore, if the vehicle is in the leftmost area of thevideo frame and the lane occupancy is low, or if the vehicle’s
tires straddle the line that demarcates the outer edge of the
roadway, it is considered to be an on-street parked vehicle.
IV. S YSTEM OVERVIEW
In this section, we describe the proposed system. An
overview of the system is shown in Fig1. The input consists ofdashboard camera video and the data of the vehicle (recordingcar) from which the video was recorded. Also, when installedin the vehicle, the inputs consist of a camera video connectedto the edge device, and the sensor values. The output isvisualized on a map and all of these processes are performed
entirely on the edge device.
A. System Requirements
We describe the requirements for a real-time on-street
parking detection system using an edge device. The three main
requirements of the proposed system are listed below.
• A simple edge device that can be installed in a vehicle• Real-time processing for object recognition and parkingposition acquisition• Ability to recognize a wide range of on-street parkingcars with high accuracy
For the first requirement, the device must be simple so that it
can be installed in a vehicle to perform on-device processing.
Previous studies experienced problems with equipment, suchas the need for large equipment to be installed in vehicles [4].
In this study, we used an NVIDIA Jetson platform, a compact
publicly available, vehicle-mountable device to fulfill the firstrequirement. Sensing is performed by a simple device thatmimics a dashboard camera that can capture video, vehicle
acceleration, and GPS information by connecting a camera toa sensor module.
For the second requirement, real-time recognition and rapid
location of parking positions are required to accurately identify
instances of on-street parking. Previous studies experienced
problems with real-time processing, such as when scenes need

to be compared with images that have been captured previously [9]. In this study, scene recognition and processing was
performed on the device to satisfy the second requirement. We
used a lightweight recognition model and a learned decisionmodel to restrict processing to on-street parking positions only,
while taking pictures using a device similar to a dashboard
camera. The GPU-equipped device increases the processing
speed and reduces the processing load by converting image
data to text data and then inputting them into the decisionmodel. Also, we reduced communication loads by uploading
the location information of on-street parked vehicles only tothe real-time database. By reducing the communication loadof each process, we can achieve real-time performance fromvehicle recognition to visualization.
For the third requirement, it was necessary to identify
vehicles with high accuracy, even in a wide range of complex
situations, from a moving vehicle. Previous studies experienced problems with vehicle identification, such as only
being able to recognize a limited range of and number ofvehicles [8]. In this paper, to satisfy the third requirement, weanalyzed many dashboard camera videos provided by a taxi
company and used them to create a decision model capableof learning vehicle data captured in a variety of situations tomake accurate decisions. Also, we intend to transfer-learn theobject recognition model to improve recognition accuracy.
B. System Configuration
The camera and sensor module was attached to a recording
device to record and acquire various types information. Then,
real-time vehicle recognition was performed using an object
recognition model. The coordinates of the Bounding Box (BB)that are assigned to a vehicle at the time of recognition are
acquired in chronological sequence and converted to text data
for each vehicle. In addition, the speed and acceleration of
the device with the BB information is also calculated andstored chronologically. With this information as inputs, weused a trained model for on-street parking decisions. Using
the trained model, if a vehicle is on-street parking, then thelocation information of that vehicle is stored for mapping. The
entire flow from recognition to processing is performed on theedge device.
C. Devices
The platform used was a Jetson TX21 , an embedded singleboard computer produced by NVIDIA. The specifications of
the Jetson TX2 platform are given in table I. The Jetson TX2
was selected as the AI computing device for this study becauseof its portable size, light weight, and because it is a GPUedge IoT device. Also, using the Development Tool Kit for
the Jetson TX2, a variety of connectors can be supported.
In automotive devices that are intended for use with sensor
modules, scalability was also a consideration.
1 Jetson:https://www.nvidia.com/ja-jp/autonomous-machines/embeddedsystems/

Construction of a street parking detection model

Input

Data
matching
Bounding box information and text dataset
Videos recorded bya dashboard camera

Assignment of bounding box by object detection model

Outputof recording vehicle information
at the time of detection

and text data of coordinate information

Street Parking Detection

Detection
Results

0：Not On-Street Parked
VehicleCreation of a Street Parking
Detection Model based on
Machine Learning Method
1：On-Street Parked
VehicleText data of recording vehicle
information corresponding to the video

Information of recording vehicle is stored
when a bounding box is detected, in chronological orderFig. 3. Constructing a street parking decision model

TABLE ID EVICE S PECIFICATIONS
Item
Size
WeightOS
CPUGPU
MemoryConnector
BluetoothWireless LAN
Power
Consumption

Jetson TX2
87×50mm, 170×170mm(board)
85 g (core)
UbuntuDual-Core NVIDIA Denver 2
Quad-Core ARM Cortex-A57
256-core NVIDIA Pascal™ GPU
with 256 NVIDIA CUDA cores
8GB 128-bit LPDDR4 Memory3
USB, HDMI, SD, Ethernet
Bluetooth
IEEE802.11ac
5.5-19.6V
7.5W|15W

Fig. 4. BB coordinates

B. Features
V. A N ON - STREET PARKING DECISION MODEL
In this section, we describe the construction of a model for
detecting on-street parking. The model building procedure isshown in Fig3. To decide whether the vehicle is parked in
the street, we used an object recognition model YOLOv3 toacquire information such as the coordinate value of the BB
and the acceleration of the recording vehicle (device) at thetime [10]. We then performed feature generation and createda training model, and made a decision using the model.
A. Data
A total of 1,765 vehicles were used to train the model using
12 videos (about 24 minutes) captured by dashboard cameras.
The original size of the video, which was 1280×720 [px], wasresized to 512×288 [px] while maintaining the 16:9 aspectratio to reduce the computational load. Most of the video wascaptured in Kyoto Prefecture on sunny and cloudy days. A
breakdown of the video results revealed that there were 56 onstreet parked vehicles and 1,709 vehicles that were not parking
on the street. However, this ratio varies depending on the timeof year and time of day.

The features used in this study are listed in table II. For thefeatures, we used the coordinate information of the BB that
was assigned to the recognized vehicle, and the speed andacceleration of the recording vehicle (device). The coordinate
values of the BB, as a feature, are shown in Fig4. A total offour BB coordinates are captured: the upper-left and lowerright X- and Y-coordinates. These coordinates were used tocreate features, such as width, height, and area for each
recognized car. The maximum, minimum, mean, and varianceof the coordinates, as well as the vehicle information werethen used to create 45 features. We constructed a trained modelusing the features.
C. Constructing a learned model
For the analyzed data, we sampled cars randomly to perform
equilibration. The ratio of off-street and on-street parking wasbalanced and trained by Random Forest (RF) and Logistic
Regression (LR) methods. We used scikit-learn, a Python
machine learning library, to construct the model. For themodel parameters, we used the default settings of the scikitlearn library [11]. The Hold-out and Stratified K-fold CrossValidation methods were used for data set partitioning andTABLE II
F EATURES
DomainFrame
Bounding Box

Recording car

Features name
Number
Top left X-coordinate
Top left Y-coordinate
Bottom right X-coordinate
Bottom right Y-coordinate
Width
HeightArea
Speed
Acceleration(x,y,z direction)

Top 15 features contributing to the decision
1. lt_x_var
2. rb_x_min
3. lt_x_mean
4. rb_x_max
5. rb_x_mean
6. lt_x_max
7. lt_x_min
8. b_w_var
9. b_h_var
10. G_y_min
11. rb_y_var
12. b_area_max
13. rb_x_var
14. b_h_mean
15. b_area_mean

TABLE IIIR ECOGNITION RESULTS FOR EQUILIBRIUM DATASETS
MethodRF-holdout
RF-5-foldLR-holdout
LR-5-fold

Accuracy
0.94
0.92
0.90
0.88

Precision
1.00
0.92
1.00
0.93

Recall
0.88
0.92
0.81
0.82

F-measure
0.93
0.92
0.90
0.87

validation. In this study, the ratio used for the holdout method
was set to 7:3 and the value of K for the K-split validation
method was set to 5.
VI. ACCURACY AND ANALYSIS / PROCESSING SPEED
In this section, we describe the recognition accuracy of
on-street parking by a trained model. We also examine thespeed of video analysis for creating a learning model and theprocessing speed required to recognize instances of on-street
parking using the learning model.
A. Recognition accuracy
The recognition accuracy of the trained model described in
the previous section is shown in table III. The maximum Fvalue of 93% was confirmed by the RF holdout method. As
a whole, all four results showed F-values of approximately
90%, suggesting that it is possible to recognize on-street
parking using the model. The importance of the features in
the RF-holdout with the highest F-value is shown in Fig5. The
contribution of features to the decision of on-street parking,especially those related to the X-coordinates of the BB, were
high. Since a characteristic of the position of the street parking,and the position of the recording vehicle is considered to havea marked effect on the decision.
B. Analysis speed
The analysis of the dashboard camera videos was performed
using the Google Colaboratory (Colab) GPU environment,
which had an average analysis speed of 15 [fps] for 12 videos.
The PC used was a MacBook Pro 2018 with 8 GB of memory,and the processing time was approximately 1.5 [fps].
C. Processing speed
We used three object recognition models, YOLOv3,
YOLOv3-tiny, and YOLOv4 and ran them on PC and Jetson

0.00

0.02

0.04

0.06

0.08

0.10

0.12

Fig. 5. Feature importanceTX2 platforms to determine the time required, in frames
per second (fps), for recognition and processing using thetrained model [12]. The PC environment was the sameas that described in the previous section. The results areshown in Table IV. The target video was captured on amain street and multiple vehicles were detected successfully.
The original video was 1280×720[px]∗ and was resized to1024×576[px]∗∗ and 512×288[px]∗∗∗ . The fps was calculated
as the average of the 10th, 20th, and 30th frames of the movie.
The results showed that YOLOv3-tiny achieved a near-realtime recognition speed on both PC and Jetson TX2 platforms.
No significant differences in processing speed were observed
between platforms in the other two models. Reducing thevideo resolution improved the processing speed, especiallyfor YOLOv3-tiny. In the video, the time required by the
Jetson TX2 was approximately 7 [fps], which is sufficient for
real-time recognition and processing. We therefore selected
YOLOv3-tiny for recognition and processing on the edge
device, and we performed real-time object recognition by
reducing the resolution of the acquired video.
VII. L OCATION V ISUALIZATIONIn this section, we describe the visualization of on-street
parking. Specifically, the process involved in acquisition oflocation information of vehicles that have been identified as
being parked in the street and their visualization on a map.
Visualization makes it easy for a third party to identify thelocation of an on-street parked vehicle and to take effective
countermeasures. The procedures from acquiring location information to visualizing data are shown in Fig6.
First, location information of on-street parked cars is obtained and stored in a real-time database. To reduce thecommunication load on the database, location information
is uploaded as text data. We also reduced the frequency ofcommunication by updating the database only with newlyTABLE IV
R ESULTS OF RECOGNITION AND PROCESSING
SPEED COMPARISONS

DeviceModel

PC

Jetson TX2∗∗

1.73 [fps]1.54 [fps]∗∗∗∗1.79 [fps]1.67 [fps]∗∗∗∗∗∗1.89 [fps]1.90 [fps]∗∗8.14 [fps]3.84 [fps]9.10 [fps]∗∗
4.89 [fps]∗∗∗∗∗∗∗∗
10.89 [fps]6.99 [fps]∗∗0.63 [fps]0.81 [fps]∗∗0.63 [fps]0.81 [fps]∗∗∗∗∗∗∗∗
0.66 [fps]0.90 [fps]YOLOv3

YOLOv3-tiny

YOLOv4∗1280×720 [px]1024×576 [px]∗∗∗512×288 [px]∗∗Positional data retention
Post data

ACKNOWLEDGMENT

Retrieve data

Positional data ofan on-street parked car

and then recognizes and processes the video on an edge device
installed in the same vehicle. The study focused primarily on
the following aspects of the system: (i) model construction
by learning from dashboard camera video, (ii) processing thevideo using the learned model on an edge device, and (iii)
visualization of the on-street parking location.
We confirmed that the trained model recognized on-street
parking with an F-value of 93% and a processing speedof 7 [fps] on the edge device.We also implemented a flowfrom recognition to visualization of on-street parking using acommon edge device. From the above, we considered that thisstudy was able to improve the three problems that were notsolved in the related studies mentioned above.
In the future, it will be necessary to increase the numberof training samples to provide a more versatile decisionmodel. Improvement of the processing flow is also necessary
to improve real-time recognition and decision making. As anext step, the entire system will be mounted on a vehicle tostreamline the process from recognition to visualization.

Location visualization

The videos used in this study were provided by DENSOTEN Limited.
R EFERENCES

ServerFig. 6. Visualization of on-street parking location

detected vehicle data. The stored location information is sent
to a webserver and visualized on a map in real-time using
socket.io.
The on-street parking location visualization system can beinstalled in multiple vehicles for more accurate and extensive coverage. Previous studies have experienced difficulties
disseminating location information to multiple vehicles. Thissystem provides everything from on-street parking recognition
to location visualization using a single system, and can beinstalled in multiple vehicles. However, vehicles in traffic
jams or vehicles waiting at traffic lights may be misidentified
as vehicles that have parked in the street. In such situations, detection using several independent systems operating
simultaneously could increase the accuracy of recognition andvisualization. For example, if the first system identifies theon-street parking position and the second system subsequently
detects on-street parking at the same position, then the vehicle
is not considered to be in a traffic jam or waiting at a traffic
light. We aim to increase the effectiveness of the system bysimultaneously visualizing independently obtained on-street
parking data on a map.
VIII. C ONCLUSION
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