Trafﬁc Monitoring System Development in Jelgava City, Latvia

Vitalijs Komasilovs

, Aleksejs Zacepins

, Armands Kviesis

, Eliecer Pe

Felipe Tejada-Estay

and Claudio Estevez

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

Department of Electrical Engineering, Universidad de Chile, Santiago, Chile

Keywords:

Trafﬁc Monitoring, Video Processing, Multi-object Tracking, Motion Modeling.

Abstract:

Smart trafﬁc management and monitoring is one of the key aspects of the modern smart city. Trafﬁc ﬂow

estimation is crucial for sustainable trafﬁc planning in the city. A requirement for successful planning and

optimization of trafﬁc is vehicle counting on the streets. Surveillance video is a suitable data source for

precise vehicle counting. A solution for real-time vehicle trafﬁc monitoring, tracking and counting is proposed

in Jelgava city, Latvia. It is based on motion detection using background modeling, which is enhanced by

statistical analysis. Two-phase assessment is utilized: motion blobs are detected and tracked using custom

state machine implementation, then tracking results are passed through number of statistical ﬁlters to eliminate

false positive detections. The system demonstrates good performance and acceptable accuracy on given test

cases (about 97% accuracy for regular trafﬁc conditions).

1 INTRODUCTION

Smart city has no one absolute deﬁnition (Anthopou-

los and Reddick, 2016). In general, the smart city is

identiﬁed by the following characteristics: smart eco-

nomy, smart people, smart governance, smart mobi-

lity, smart trafﬁc, smart environment, and smart li-

ving (Mahizhnan, 1999). Smart trafﬁc consists of

several topics, which are smart trafﬁc light mana-

gement, smart accident management, smart public

transportation systems, vehicle identiﬁcation, vehicle

tracking and counting.

Precise and reliable information about trafﬁc con-

ditions can be used to synchronize trafﬁc lights, assist

drivers in the selection of routes, assist governments

in planning the trafﬁc system expansion, building new

roads, and data for designing better solutions for the

urban and road trafﬁc (Barcellos et al., 2015).

Number of researches has been reported on traf-

ﬁc monitoring and congestion analysis in urban areas.

Advanced communication technologies and Internet

of Vehicles concept are used for data acquisition and

transfer to analytical systems (Ahmed et al., 2016;

Wan et al., 2016). Indirect data sources such as GPS

tracks from probe vehicles (e.g. public transport)

are analyzed ofﬂine and trafﬁc characteristics are ex-

tracted for strategical planning purposes (Carli et al.,

2015). Multiple data sources (such as GPS tracks, in-

road and vision sensors) are fused to provide accurate

information about trafﬁc conditions to citizens (Ba-

con et al., 2011). Social media and other public infor-

mation (e.g. events, feedbacks) is applied as comple-

mentary information for trafﬁc modeling and analysis

(Wang et al., 2017).

To this moment, in Jelgava city

trafﬁc statistics

are collected manually by two approaches: a) opera-

tor manually observes the road segment or crossing

and writes down the number of vehicles; b) camera

is located near the monitored place and a video ﬁle is

recorded. Afterwards, the operator watches the video

and manually counts the vehicles. Theoretically, bus

GPS tracks could be applied for trafﬁc analysis, but

bus routes are covering very limited set of roads.

The authors propose approach to improve this pro-

cess by using IT solutions for real-time video analy-

sis without attracting the operator for manual vehi-

cle counting. The use of image-based sensors and

computer vision techniques for data acquisition on the

trafﬁc of vehicles have been intensely investigated in

the recent years (Tian et al., 2011). While intrusive

trafﬁc sensing technologies (e.g. inductive loops, so-

nar or microwave detectors on roads, GPS sensors

on vehicles) are considered more precise they have

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

ter of Zemgales region, distance from Latvia capital Riga

is 42 km.

Komasilovs, V., Zacepins, A., Kviesis, A., Peña, E., Tejada-Estay, F. and Estevez, C.

Trafﬁc Monitoring System Development in Jelgava City, Latvia.

DOI: 10.5220/0006629606590665

In Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2018), pages 659-665

ISBN: 978-989-758-293-6

659

Figure 1: An example of video frame with marked area of

interest.

major disadvantages: installation cost, trafﬁc disrup-

tion during installation or maintenance, and usually

these methods are unable to detect slow or static vehi-

cles (Mandellos et al., 2011). Contrary, surveillance

and/or security cameras are very common in urban

areas (e.g. Jelgava municipality serve more then 200

surveillance cameras in the city covering almost all

streets of the city) and are subject of access granting

negotiation rather than infrastructure installations.

Nevertheless, there are plenty of algorithms and

systems for image processing (Zhu et al., 1996; Wu

et al., 2001; Rad and Jamzad, 2005; Iwasaki and

Itoyama, 2007; Lim et al., 2009), image counting in

real situations from real-time video stream is not a

trivial task and there are challenges to solve. Unfortu-

nately, there is no one ultimate system, which can be

applied in all cases. As well price of the commercial

system can be a factor, which limits its application by

government.

Authors address underestimated availability of

video information on urban roads, which can be uti-

lized for trafﬁc condition recognition using modern

image processing technologies. This paper describes

a software solution for vehicle tracking and counting

using image processing technologies. The live video

is obtained from Jelgava municipality web page

(http://www.jelgava.lv/lv/pilseta/tiessaistes-kamera/)

from stationary camera positioned aside the road on

the building wall by the address 5 J. Cakstes Blvd.,

Jelgava, Latvia (see Figure 1).

Vehicle trafﬁc occurs in the diagonal direction,

from top right (farthest from the camera) to bottom

left (closest to the camera), and vice versa. Video has

FullHD resolution of 1920 ×1080 px at 30 frames per

second. Apart from other objects (e.g. wires, bridge,

pedestrians, buildings, etc.) video stream contains re-

gular two-way (one lane in each direction) road of Jel-

gava city.

Complexity of vehicle counting task is increased

by several aspects: vehicle occlusions occur due to

Figure 2: Principal process ﬂow chart.

camera position, area of interest includes parking lots

on both sides of street, speed bumps on a street, bridge

cables appear on foreground, semi-hidden turning un-

der the bridge.

2 MATERIAL AND METHODS

Figure 2 shows basic workﬂow of solution for vehi-

cle trafﬁc counting on live stream video. Input frames

are extracted directly from YouTube FullHD stream

(1920×1080), cropped to area of interest (576 × 648)

and pushed to further processing, described in sub-

sections below. Solution is implemented and tested

using Python 3.5.2 environment. OpenCV 3.2.0 li-

brary (Bradski, 2000) is used for low level image ma-

nipulations and processing.

RESIST 2018 - Special Session on Resilient Smart city Transportation

660

(a) MOG (b) MOG2 (c) ﬁnal mask

Figure 3: Moving vehicle masks.

2.1 Vehicle Detection

This stage includes background modeling and vehicle

detection. Proposed solution uses background sub-

traction method for motion detection on subsequent

frames. Authors used (KaewTraKulPong and Bo-

wden, 2002) algorithm implementation available in

OpenCV (BackgroundSubtractorMOG), which gave

better results on test cases comparing with (Zivko-

vic, 2004) implementation (BackgroundSubtractor-

MOG2) as shown on Figure 3(a) and (b) respecti-

vely. The parameters used are as follows: history=50,

nmixtures=3, backgroundRatio=0.1, noiseSigma=10.

For better moving blob segmentation additional

mask operations are applied:

• Gaussian blur with kernel size 25 × 25 px and

standard deviation 0;

• binary threshold on level 100;

• single erosion iteration with elliptical kernel

3 × 3 px.

The resulting ﬁnal mask after all manipulations is

shown on Figure 3(c).

At the next stage contour search is performed on

mask. The list of contours is ﬁltered by area thres-

hold t

area

= 2000: contours with area less then 2000

(number obtained empirically for given frame and

vehicle size) are considered as no-interest detections

(e.g. birds, tree leaves moving by the wind, walking

people, bicycles, scooters) and excluded from further

processing. Then center coordinates of each contour

is calculated and together with contour area and boun-

ding box coordinates is packed into tracking point

data structure and pushed to further processing.

2.2 Vehicle Tracking

Vehicle tracking stage stands for the problem of fol-

lowing same vehicle across multiple subsequent fra-

mes and includes track assignment, motion modeling

and raw track reporting steps. As an input this mo-

dule uses list of moving vehicles (contours) detected

Figure 4: Track status transitions.

on current frame and it maintains its internal state of

tracked vehicles from previous frames.

First of all detected vehicles are assigned to tracks

- the trajectory of the moving vehicle from previous

frames. Authors use modiﬁcation of Hungarian al-

gorithm for linear sum assignment problem and mi-

nimize the sum of distances between detections from

current and previous frames (1):

min

∑

i, j

(1)

where D is distance matrix between last tracked points

and current detected points, X is binary assignment

matrix which indicates pairs of detection point and

tracked point.

There are cases when between two subsequent fra-

mes some tracked vehicles left the current frame and

new (yet untracked) vehicles appeared on the frame.

For proper handling of such cases authors use dis-

tance threshold t

dist

= 300 px and area change ratio

threshold t

areaRatio

= 0.3, which in taken into account

during assignment step: detection is not assigned to

track if distance between those two is more than 300

px or if area has changed by more than ±30%. As

a result new vehicles are properly assigned to new

tracks, while old vehicles are left unassigned.

In addition to increase stability of tracking and

reduce false positive vehicle tracking cases authors

use status modeling with several time thresholds (see

Figure 4) expressed in number of processed frames.

Frames are not explicit measure of time, but because

video frame rate is considered as constant, it can serve

this purpose.

• t

new

= 15: time delay (frames) until newly trac-

ked object becomes active, helps to eliminate

shortly appearing detection blobs (e.g. shadows,

car occlusions, clouds);

Trafﬁc Monitoring System Development in Jelgava City, Latvia

661

Figure 5: Real time monitor screenshot.

• t

lost

= 30: grace period (frames) while lost track

is kept among assignment candidates, helps to eli-

minate short gaps in detection (mostly car occlu-

sions);

In order to smooth tracked vehicle trajectory and

improve detection gap handling authors apply Kal-

man’s ﬁlter for vehicle motion modeling and pre-

diction. Internal state of the tracked vehicle is mo-

deled using 6 dynamic parameters:

• x, y: object center coordinates on picture;

• v

, v

: object moving speed;

• s, v

: object contour area and its changing speed,

respectively.

As input for ﬁlter corrections x , y and s are provi-

ded from detection results, while speeds are maintai-

ned internally.

Finally, active tracks are displayed on real-time

monitor (see Figure 5) and eventually Old tracks are

archived to external database ”as-is” (authors use se-

parate CSV ﬁle for every 10 minutes of monitoring

for easier handling).

2.3 Post-processing and Filtering

For better accuracy post-processing on reported raw

tracking data is performed by separate process. This

stage contains feature extraction, track ﬁltering and

reporting. First of all for each track following features

are created:

• coordinates of tracking starting point;

• vehicle moving direction (to left or to right);

• number of recorded points in track and its length;

• imaginary mid-line crossing;

• linear motion trajectory interpolation (coefﬁcients

for line y = ax + b and R

Figure 6: Summary monitor screenshot.

Then statistical analysis is performed on resulting

dataset and criteria are derived for ﬁltering ineligible

cases. Statistical analysis is described in details in

next section.

Finally obtained statistics are periodically (every

5 seconds) displayed on summary monitor (see Fi-

gure 6).

3 RESULTS AND DISCUSSION

This section describes results and their evaluation for

the proposed solution.

3.1 Experiments Setup

For testing and evaluation purposes eight 10 minu-

tes long video clips were recorded from live stream

at different time of the day, every 2 hours from 7:00

till 21:00 (see table 1 for details). On each clip vehi-

cles are manually counted for ground truth reference.

Then each clip is processed via the proposed solution

and results are collected.

For better statistical analysis another raw tracking

dataset is gathered during several afternoons when

most of trafﬁc happens on the given street. In total

it has about 10 000 recorded tracks.

3.2 Statistical Analysis

Raw vehicle tracking reports obtained from ﬁrst stage

of processing demonstrate poor accuracy: the system

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662

Table 1: Recorded video clips.

Nr. Time Description

1 07:00 dawn

2 09:00 partly cloudy, sun reﬂections

3 11:00 cloudy

4 13:00 sunny

5 15:00 sunny, windy

6 17:00 rainy, congestion

7 19:00 partly cloudy

8 21:00 dusk, headlamp reﬂections

Figure 7: Track starting points and imaginary mid-line.

detects many false positives. Statistical analysis on

recorded dataset were performed to develop suitable

ﬁltering conditions.

First of all, tracks are ﬁltered by moving direction

and starting point coordinates in a way that tracks po-

tentially cross imaginary mid-line. Observations con-

ﬁrm that the majority of tracks started after mid-line

by moving direction are either detection errors or du-

plicates of already tracked vehicles. Figure 7 shows

spatial scatter plot of track starting points colored by

moving direction.

Next track variation is analyzed by number of de-

veloped features (see Figure 8). Number of recorded

points in track is not correlating with track quality:

there are correct tracks with small number of recor-

ded points (fast moving vehicles) as well as with large

number of recorded points (slow vehicles / conges-

tion).

Track length in pixels is good candidate for ﬁlte-

ring condition. Whole class of false positives have

short trajectories (vehicle occlusions, foreground wi-

res, etc). Observation shows that very long tracks are,

in essence, tracks of two vehicles merged into one

(track jumps between lines and goes in opposite di-

rection). Such cases can be separated into two in-

dependent tracks, but in practice these are very rare

cases. Therefore, authors count long tracks as single

track.

Figure 8: Track variation by selected features.

Another set of features used for raw data ﬁltering

is trajectory linear interpolation results (a, b coefﬁ-

cients and R

value for line y = ax + b). Coefﬁcient

of determination R

shows how much given track is

close to straight line. Despite the fact that R

varia-

tion is notably close to 1.0 there are a lot of outliners

among acceptable tracks, and vice versa, wrong tracks

with close to linear trajectory.

Coefﬁcient b affects track pitch on the picture,

which in trafﬁc context relates to vehicle position on

the street (left or right side of street). Due to presence

of speed bumps in the middle of each line drivers tend

to drive in-between of bums when there is no onco-

ming trafﬁc. Therefore this coefﬁcient is not suitable

for any track selection.

In contrast, a coefﬁcient shows linear slope of the

trajectory, which in turn should correspond to street

direction for all vehicles. Figure 8 shows relatively

low variation in a coefﬁcient values, and selected out-

liner cases are clearly incorrect.

Figure 9 demonstrates samples of tracks selected

by different criteria. After statistical analysis follo-

wing criteria is used for selecting acceptable tracks

out of raw tracking results:

• track starting point is before mid-line by vehicle

moving direction;

• track length L > 100 px;

• slope of linear interpolation is in range −2 < a < 1

3.3 Accuracy Evaluation

Table 2 shows summarized accuracy evaluation re-

sults on all test cases. For each test case ground

truth numbers are manually counted and considered

as accuracy reference. Total number of raw tracks

obtained from the ﬁrst stage of processing are shown

in the table (at this stage they are not divided by di-

rection). Next numbers correspond to tracks left after

Trafﬁc Monitoring System Development in Jelgava City, Latvia

663

Table 2: Accuracy evaluation summary.

Ground truth Raw tracks Statistical analysis Final accuracy

Nr. to left to right count accuracy to left to right to left to right

1 25 13 39 97% 25 13 100% 100%

2 63 25 96 91% 61 24 97% 96%

3 55 23 82 95% 54 23 98% 100%

4 50 54 107 97% 50 55 100% 98%

5 52 30 85 96% 51 32 98% 93%

6 80 71 188 75% 61 73 76% 97%

7 49 29 82 95% 47 31 96% 93%

8 37 18 78 58% 39 19 95% 94%

Figure 9: Track types selected by statistical analysis.

analysis and ﬁltering. Last two columns show evalua-

tion of ﬁnal accuracy of the system. Overall solution

has acceptable accuracy for given task and conditions

(about 97% accuracy for regular trafﬁc conditions).

There are two notable cases worth to highlight.

Test case 6 is recorded at 17:00 when trafﬁc con-

gestion happened on the given street. Proposed so-

lution relies on motion detection via background mo-

deling and due to congestion and slow vehicle mo-

vement it was not able to properly track separate

vehicles (stopped vehicles were considered as back-

ground and movement phase was too short for star-

ting tracking). As results show, statistical analysis

does not help to resolve these cases. An impro-

vement option would be to keep tracking even stop-

ped vehicles by implementing difference measure be-

tween last seen motion blob and current still frame

(e.g. keypoint matching or feature histograms) which

is out of scope of current research.

Another notable test case 8, which was recorded

at 21:00 shortly after rain. Vehicle headlamps are re-

ﬂecting from wet street surface, which leads to very

high number of false positives in raw tracking results

(almost all vehicles going left are counted twice: he-

adlamp reﬂection and vehicle itself). However statis-

tical analysis and raw result ﬁltering helps to signiﬁ-

cantly improve results: from 58% to 94%.

In current system implementation the only pro-

cessing outcome is number of vehicles moving each

direction. Given that detailed vehicle tracking infor-

mation is archived, it is possible to develop and ex-

tract more useful trafﬁc features. Vehicle spatial posi-

tion on camera image could be mapped to geographi-

cal position, which gives potential to estimate vehi-

cle speed. Vehicle snapshots could be used for furt-

her classiﬁcation task (e.g. cars, vans, buses, tucks).

In case of outputs of multiple cameras are fused into

common database there is potential to estimate traf-

ﬁc density and even track distinct vehicles across the

city.

4 CONCLUSIONS

Trafﬁc ﬂow monitoring solution for real-time vehi-

cle counting on live stream is proposed, developed

and tested for Jelgava city in Latvia. It is based on

background modeling, multi-vehicle tracking and sta-

tistical analysis. The experimental results show that

proposed solution is suitable for variety of trafﬁc and

weather conditions, except congestions.

The proposed method detected the number of cars

with an accuracy ranging between 93 − 100% for re-

gular trafﬁc conditions and 76% for congestion.

To extend system functionality it is planned to im-

plement vehicle classiﬁcation module (e.g. car, van,

bus, truck, motorcycle, etc). Also for better conge-

stion handling it is planned to improve vehicle de-

tection approach in addition to background modeling.

Camera positioning is crucial factor for precise

trafﬁc monitoring and it should be mounted in more

elaborate location without any interfering objects

(e.g. wires, pillars, bridges, etc).

Visual image processing and analyzing techniques

are improved during recent years. Outdoor surveil-

lance cameras have underestimated potential for non-

intrusive trafﬁc monitoring and feature extraction in

urban areas.

RESIST 2018 - Special Session on Resilient Smart city Transportation

664

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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