Implementation of Smart Parking Solution by Image Analysis

Aleksejs Zacepins, Vitalijs Komasilovs and Armands Kviesis

Department of Computer Systems, Faculty of Information Technologies, Latvia University of Agriculture, Jelgava, Latvia

Keywords:

Smart City, Smart Parking, Video Processing, Image Analysis.

Abstract:

Modern smart city concept implies various smart aspects including smart parking management. Searching

for a free parking lot can be a challenging task, especially during major events, therefore automatic system,

which will help drivers to ﬁnd a free parking is very valuable. There are many intrusive and non-intrusive

technologies available for smart parking development, but authors of this paper developed a system based on

video processing and analysis. Authors developed Python application for real-time parking lot monitoring

based on video analysis of public video stream. Five classiﬁer models (Logistic Regression, Linear Support

Vector Machine, Radial Basis Function Support Vector Machine, Decision Tree and Random Forest) were

compared for parking lot occupancy detection. Logistic regression classiﬁer showed better results and was

chosen for real-time parking monitoring application. System shows good performance and correctly predicted

parking lot occupancy almost in all test cases.

1 INTRODUCTION

Smart City concept is highly dependent on the use of

Information and Communication Technologies (ICT)

for a more efﬁcient use of existing resources, with

main aim to improve citizens quality of life (Albino

et al., 2015). As far as the livability of cities is con-

cerned, trafﬁc is one of the most frequent and complex

factors directly affecting citizens (Sevillano et al.,

2014).

In Latvia more than 3 000 new vehicles are

registered each month, but road infrastructure is

not developing so quickly. There are 664 177

of passenger cars on the Latvian roads (based

on Road Trafﬁc Safety Directorate statistics:

https://www.csdd.lv/en/vehicles/statistics-of-

registered-vehicle) and there are insufﬁcient car

parking facilities especially during the major concerts

or sport events. Development of Smart parking

can minimize the parking problem in modern cities

(Pham et al., 2015).

Smart parking systems are implemented world-

wide, mainly in Europe, United States and Japan

(Shaheen, 2005; Kuran et al., 2015; Lan and Shih,

2014). Implementation of smart parking systems has

many advantages for municipality, parking owners

and drivers. Drivers can easily ﬁnd vacant park-

ing lots and avoid driving to fully occupied parkings

(Shoup, 2006; Idris et al., 2009; Polycarpou et al.,

2013), this also minimizes the air pollution (Arnott

and Inci, 2006).

Smart parking systems usually are divided into

several categories: parking guidance and informa-

tion system (PGIS), transit based information sys-

tem, smart payment system, E-parking and automated

parking (Shaheen, 2005). Each system has its advan-

tages and disadvantages. This paper deals with park-

ing guidance and information system sub-component,

named parking lot occupancy detection.

Critical point in smart parking operation is park-

ing lot occupancy detection. There are many tech-

nologies available for this, which are mainly divided

into two categories: intrusive (inductive loops, piezo-

electric cables, active infrared sensors, etc) and non-

intrusive (passive infrared sensors, ultrasonic, video

image processing, etc).

This paper describes software solution for parking

lot occupancy monitoring using video processing and

image interpretation methods. Many authors tried to

develop smart parking system based on video analysis

(Sevillano et al., 2014; Al-Kharusi and Al-Bahadly,

2014), but still there are no one universal system,

which can be used in all parking cases. This solu-

tion can extend the existing Smart parking solutions

in Jelgava city

, which are based on inductive sensors

Jelgava is the fourth largest city in Latvia, is historical cen-

ter of Zemgales region, distance from Latvia capital Riga

is 42 km.

666

Zacepins, A., Komasilovs, V. and Kviesis, A.

Implementation of Smart Parking Solution by Image Analysis.

DOI: 10.5220/0006629706660669

In Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2018), pages 666-669

ISBN: 978-989-758-293-6

Figure 1: Frame example from live video stream.

for each parking lot monitoring and video monitoring

of entering and exiting cars (Zacepins et al., 2017).

For demonstration purposes the live

video is obtained from public video stream

(https://balticlivecam.com/cameras/estonia/narva/)

from ﬁx camera positioned above the parking in

Narva, Estonia (see Figure 1).

2 MATERIAL AND METHODS

Figure 2 shows basic workﬂow of solution for parking

lot monitoring on live stream video. Input images are

extracted directly from FullHD stream (1920 ×1080),

cropped to area of interest and used for further pro-

cessing and analysis, described in subsections below.

Solution is implemented and tested in Python

3.5.2 environment using different provided libraries

(cv2, sklearn.linear model, numpy, matplotlib, etc).

OpenCV 3.2.0 library (Bradski, 2000) is used for

low level image manipulations and processing (im-

age scaling, resizing, changing image color schema,

image saving and opening, etc).

2.1 Model Development

This paper describes parking lot status (free or occu-

pied) detection approach based on machine learning

techniques. Extensive phase of sample image prepa-

ration is crucial for precise model development.

First of all variety of example images where cap-

tured from live stream video camera. Authors used

custom made automated utility which connected to

the video stream and downloaded FullHD frames

(1920 × 1080 px) on deﬁned interval (every 10 min-

utes). More than 2 100 images where captured during

two weeks, which gave good variety in weather and

parking conditions (different time of the day, day of

the week).

Next, manual region of interest identiﬁcation took

place. The captured frames contain overhead view of

Figure 2: Parking status monitoring process workﬂow.

parking with about 130 lots. Vehicles are oriented in

different angles and occlusions are common. During

daytime a lot of buses are using the parking. More-

over car drivers often disobey parking lot markup

(two cars in one lot, parking outside and across lots).

For demonstration purposes few parking lots were se-

lected and analyzed as described below. All other

parking lots can be processed following the same pro-

cedure.

Deﬁned areas of interest (selected parking lots)

are cropped from example images and pre-processed.

First, parking lot images are scaled down to uniform

size of 50 × 30 px which allows generic processing

of different lots. In order to decrease number of in-

put parameters for further modeling images are also

converted to gray-scale color schema.

Training and validation datasets are created from

normalized gray-scale images (pixel color values c ∈

[−1 : 1]) of speciﬁc parking lots. Due to lack of night

mode in selected live camera images captured from

20:00 till 8:00 where excluded from datasets. Images

are manually separated into two classes: positive (car

exists, parking lot is occupied) and negative (park-

ing lot is free without car) (see Figure 3). There are

2 000 samples in positive and 1 000 samples in neg-

ative datasets. 25% of randomly selected samples are

used for validation purposes, while others are used for

model training. For better handling training and vali-

dation image sets were converted to numeric arrays.

Five classiﬁcation models from sklear n package

were trained for image classiﬁcation by two classes:

Implementation of Smart Parking Solution by Image Analysis

667

(a) (b) (c)

(d) (e) (f)

Figure 3: Positive (a, b, c) and negative (d, e, f) image sam-

ples.

either parking lot is free or occupied. Authors com-

pared classiﬁers modes without any tuning or im-

provement. Selected models with parameters are:

• Logistic Regression (LR) with default parameters;

• Linear Support Vector Machine (l-SVM) with

kernel = linear and C = 0.025;

• Radial Basis Function Support Vector Machine (r-

SVM) with gamma = 2 and C = 1.0;

• Decision Tree (DT) with max depth = 5;

• Random Forest (RF) with max depth = 5 and

max f eatures = 1.

Due to model implementation speciﬁcs input im-

ages (matrices) are converted to vectors. Model com-

parison and precision analysis are described in details

in results section. Finally based on comparison results

one model with highest precision was chosen for real-

time parking monitoring.

2.2 Parking Lot Monitoring

The model selected in previous phase is used for on-

line parking status monitoring. Current input frame

is captured from live video stream. Then it is pre-

processed in a way similar to model building phase:

regions of interest (parking lots) are cropped out of

the frame, scaled to uniform size and converted to

gray-scale color schema.

Next each captured region is processed through

the model, which returns its prediction: probabilities

of each class indicating either certain parking lot is

free or occupied. Regions with ”occupied” probabil-

ity P(car) ≥ 0.6 are considered as occupied (this pa-

rameter can be manually adjusted depending on user

requirements).

Results of classiﬁcation are reported as status of

parking lot. All analysis and processing take less than

1 sec (connection establishment to the live stream

takes most of the time) and therefore such system is

applicable for real-time parking lot monitoring.

Table 1: Comparison of classiﬁcation models by scores (%).

Num of

samples

Classiﬁcation model

LR l-SVM r-SVM DT RF

100 90.8 84.0 65.8 85.7 76.7

500 93.5 88.8 70.6 87.7 86.8

1 000 93.4 90.4 75.0 88.8 89.9

1 500 93.1 90.4 77.2 90.1 89.6

2 000 92.9 90.4 77.7 92.0 85.5

Figure 4: Classiﬁer score depending on number of training

samples.

3 RESULTS AND DISCUSSION

Model development and classiﬁer selection is impor-

tant task. To select appropriate model for given task

ﬁve classiﬁers were trained on the same dataset and

evaluated. Training and testing datasets are distinct

and image samples do not overlap over datasets. Ta-

ble 1 summarizes scores (mean accuracy of predic-

tion) of all selected classiﬁers trained with different

numbers of sample images.

Logistic regression (LR) classiﬁer has highest av-

erage score and is not signiﬁcantly affected by num-

ber of sample images. Linear SVM (l-SVM) and De-

cision Tree (DT) classiﬁers has comparable scores

and slightly improve average score depending on

number of samples. RBF SVM (r-SVM) has signiﬁ-

cantly lower score compared to others, while it shows

noticeable increase of score for whole range of tested

sample numbers. Random forest (RF) classiﬁer has

ﬂuctuations of scoring values and is comparable with

l-SVM and DT. Detailed plot of classiﬁer score de-

pending on number of training samples is shown on

Figure 4.

Based on classiﬁer comparison Logistic regres-

sion were selected for live parking status monitoring

application. According to obtained results there is no

signiﬁcant increase of classiﬁer precision after 300

training samples, therefore smaller training set can

RESIST 2018 - Special Session on Resilient Smart city Transportation

668

Figure 5: Parking lot status report.

be used for parking status detection with precision of

90% and more.

Results can be considered as good taking into ac-

count camera position, parking lot conﬁguration and

drivers’ parking habits. Figure 5 shows an example

of parking status report. Frame colors indicate either

parking lot is free (green) or occupied (red). Percent-

ages show probability of lot occupancy P(car) calcu-

lated by model.

4 CONCLUSIONS

Results show that classiﬁcation precision is not signif-

icantly dependent on number of image samples used

for model training (about 300 samples is enough for

desired precision).

Disadvantage of video based parking monitoring

approach is that if car is not parked directly at consid-

ered lot, than system will not detect car correctly. In

case parking lots are differently oriented to camera,

than several models have to be trained for each park-

ing lot orientation. In future it is planned to extend

this solution for whole parking monitoring.

Signiﬁcant advantage of video based parking

monitoring is that existing infrastructure can be used:

already installed surveillance, security or other cam-

eras can be used for image acquisition.

For better results pixel spatial location aware mod-

els should be used (e.g. convolutional neural net-

works, histogram of oriented gradients, etc).

ACKNOWLEDGMENTS

Scientiﬁc research, publication and presentation are

supported by the ERANet-LAC Project Enabling re-

silient urban transportation systems in smart cities

(RETRACT, ELAC2015/T10-0761).

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