1. 1

2. Characterization of vehicle flow for intelligent
transportation systems
People’s Democratic Republic of Algeria
Ministry of Higher Education and Scientific Research
M’Hamed BOUGARA University – Boumerdes
Institute of Electrical and Electronic Engineering
Department of Electronics
MASTER
In Electrical and Electronic Engineering
Option: Telecommunications
Title
Presented by:
•Abdenour BOUAICHA
Supervisor:
Dr. Fatma KEROUH

3. Outline
Introduction
Intelligent Transportation Systems
 Problems of visual based systems
Proposed system
Results and discussions
Conclusion and perspectives
3

4. Introduction
Problem statement
The solution???
4

5. Intelligent Transportation
systemsWhat /why ITS?
5

6. The major categories of ITS?
Intelligent
Transportation
Systems
Advanced Traffic
Management
Systems
Advanced
Travelers
Information
Systems
Commercial
Vehicle
Operation
Advanced Public
Transportation
Systems
Advanced
Vehicle Control
Systems
Advanced Rural
Transportation
Systems
6
Intelligent Transportation
systems

7. ITS classes?
Radar Camera
GPS
GPRS
…
7
Intelligent Transportation
systems
Non visual
based systems
Intelligent
Transportation
Systems
Visual based
Systems

8. Video quality Software
Blur effect Complexity
Bad illumination Execution time
Illumination changing …..
Shadow
Noise
Multiple vehicle occlusion
…..
8
Problems of visual based systems

9. Problems of visual based systems
ITS needs Information
Information requires Data.
Data relies on smart video surveillance.
Fast Robust Reliable
9

10. Intelligent Transportation
Systems
Our purpose?
Why?
• Accumulating the statistics.
Identifying critical flow time periods.
Determining the influence of large vehicles or pedestrians
on vehicular traffic flow.
Knowing the reasons of traffic congestion, road
degradation, and air pollution.
…… 10
Surveillance
camera
Image processing
techniques
Vehicle
detection,
counting,
classification and
speed estimation

11. Proposed vision based ITS
11
-Vehicle detection
and counting.
-Vehicle
Classification.
- Vehicle speed
estimation.
Processing
Preprocessing
Video

12. Proposed ITS(preprocessing)
12
Color
conversion
Read the frame LBP Selecting ROI

13. Proposed ITS(preprocessing)
13
Middle
Best ROI Inside the lane
Neither greater nor Small

14. Selected best fixed location of ROI’s
14
Proposed ITS(preprocessing)

15. Proposed vision based ITS
15
Start
The camera is
activated
Preprocessing
The vehicle passing the
ROI
Vehicle detection, counting, classification,
and speed estimation
i++ Yes
No
Yes
End
No

16. Proposed ITS (Detection, counting, and
classification)
16
Proposed idea: Vehicle detected
Vehicle counted
and classified

17. Proposed ITS (Detection and
counting)
17
Start
Read ref
Preprocessing
Camera is
activated
Read rf
Preprocessing
Compute D
D >Th1
&&
D<=Th2
Count++End
Yes
No
Yes
No

18. Proposed ITS (Detection and counting)
18
Thresholds VS noise
number of rames
D

19. Proposed ITS (Classification)
19
Start
Read ref
Preprocessing
Camera is
activated
End
CCF++
Vehicle is
detected
Vehicle
is
counted
CCF >=
Th3
Big
Small
No
No
No
No
Yes
Yes
Yes
Yes

20. Proposed ITS (Speed estimation)
20
Proposed idea:

21. Proposed ITS (Speed estimation)
21
Start
Camera is
activated
End
Vehicle is
counted in
first ROI
Vehicle is
counted in
second
ROI
COUNT2
COUNT1
COUNT1 !
= COUNT2
Spd++
Calculate the
Speed
Yes Yes
Yes
Yes
No
No
No
No

22. Experiments, Results and
Discussions
Video1 Video2
Video3 Video4
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23. Experiments, Results and
DiscussionsThe characteristics of the tested videos
The video
settings
Video1 Video2 Video3 Video4
Frame rate 14.999 25 30 30
Number of
frames
500 750 878 1071
Duration 33.34 30 29.70 36.13
Height 240 240 720 720
Width 320 320 1280 1280
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24. 24
Experiments, Results and Discussions
Bad
illumination
Blurring
occlusoion
Invariant
illumination
Strongest
shadow
Vibration of
camera

25. Noise Bad
illumination
Illumination
changing
shadow occlusion Blur Fixed
camera
Video1 Y Y N Y N Y N
Video2 Y N Y Y Y Y N
Video3 Y N N Y N Y Y
Video4 Y N N Y N Y Y
25
Experiments, Results and
Discussions

26. Experiments, Results and
DiscussionsVehicle detection and counting
ROI Video 1 Video 2 Video 3 Video 4
GT OR GT OR GT OR GT OR
ROI 1 13 13 13 13 5 6 4 4
ROI 2 6 6 9 9 9 9 17 14
ROI 3 12 12 8 8 15 14 20 15
ROI 4 13 13 13 13 16 16 22 26
Total 44 44 43 43 45 45 63 59
Accuracy 100% 100% 95.56% 93.65%
Runtime of
one
frames(sec)
0.0702 0.0467 0.1747 0.1713
26

27. Experiments, Results and
Discussions
Vehicle detection and classification
ROI Video 1 Video 2 Video 3 Video 4
S B RS RB S B RS RB S B RS RB S B RS RB
ROI 1 13 0 13 0 8 1 9 0 5 0 5 1 4 0 4 0
ROI 2 5 1 5 1 9 0 9 0 7 2 7 2 15 2 10 4
ROI 3 12 0 12 0 7 1 8 0 15 0 14 0 19 1 12 3
ROI 4 13 0 13 0 10 3 13 0 12 4 7 9 22 0 23 3
Accuracy
rate
100% 88.37% 88.88% 88.88%
Runtime
of one
frame
(sec)
0.0710 0.0463 0.1712 0.1737
2 27

28. Experiments, Results and
Discussions
Vehicle detection and speed estimation(Km/h)
The vehicle ROI1 ROI2 ROI3 ROI4
1 59.85 39.9 59.85 119.7
2 59.85 59.85 59.85 59.85
3 59.85 59.85 59.85 39.9
4 59.85 119.7 59.85 119.7
5 119.7 39.9 39.9 39.9
6 59.85 59.85 39.9 59.85
7 59.85 59.85 59.85 119.7
8 59.85 59.85 59.85 119.7
9 59.85 29.925 - 119.7
10 39.9 - - 119.7
11 39.9 - - 119.7
12 59.85 - - 119.7
13 59.85 - - 119.7
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29. Experiments, Results and
Discussions
Vehicle detection and speed estimation (Km/h)
The
counte
r
ROI1 ROI2
1 39.91 39.9075
2 39.91 39.9075
3 53.21 35.4733
4 53.21 35.4733
5 53.21 35.4733
6 53.21 63.872
7 - 63.852
8 - 53.21
9 - 53.21
The
counter
ROI1
1 40.21
2 40.21
3 40.21
4 106.73
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30. Video The task The
running
-time
Percentage
accuracy
Count Class Speed Coun
t
Class
Video 1 Y Y - 0.0782 100% 100%
Video 2 Y Y Y 0.0938 100% 90.91
%
Video 3 Y Y Y 0.1901 95.56
%
88.88
%
Video 4 Y Y Y 0.2031 93.85
%
88.88
%
Total - - - - 97.3% 92.42
%
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Experiments, Results and Discussions

31. 31
Final Project

32. Analyzing the proposed approach
The strengths
Real time
Simple
Robust against to bad and changing illumination
Robust against to the noise
Robust against to the blur effect
Robust against to the occlusion
Simple camera
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33. The weaknesses
Vehicle passing through portion of ROI.
Fixed camera.
Vehicle congestion.
33
Analyzing the proposed approach

34. Conclusion and perspectives
 Introducing the concepts of ITS.
 Showing the problems on visual based systems.
 Developing a simple and real-time ITS that detects, counts, classifies
and estimates the speed of vehicles.
 Proposed ITS relies on selected best ROI which in middle and inside
of the lane with neither small length nor big.
 Separating the ROI solved problem of multiple vehicles occlusion.
 Using LBP have shown that our system is robust against bad and
changing illumination.
 Using adaptive thresholds solve the problem of noise and blur effect.
 The percentage accuracy of counting is: 97.3%.
 The percentage accuracy of classification is: 92.42%
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35. Conclusion and perspectives
Perspectives:
-Test the proposed system on long videos that
contains more challenging problems.
-Consider more classes in the classification step
(small, medium, and big vehicles).
-Acquire videos with ground truth to test deeply the
speed estimation part.
-Plan to build android application.
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