UrRehman, Yasar Abbas, Adam Khan, and Muhammad Tariq. "Modeling, design and analysis of intelligent traffic control system based on integrated statistical image processing techniques." Applied Sciences and Technology (IBCAST), 2015 12th International Bhurban Conference on. IEEE, 2015.

1. Modeling, Design and Analysis of
Intelligent Traffic Control System
Based on Integrated Statistical
Image Processing Techniques
Yasar Abbas Ur Rehman
Department of Electrical Engineering
City University Peshawar
FAST, NUCES Peshawar Campus

2. Outline
• Introduction
• Problem Statement
• Proposed Solution
• Results
• Conclusion
2

3. Introduction
• Monitoring and control of intercity traffic
▫ Not a trivial problem
▫ Careful planning
▫ Increase in road infrastructure?
▫ Availability of Technological Assets
• Camera controlled monitoring and control
▫ Vehicle flow
▫ Speed calculation
▫ Automatic Number Plate Recognition (ANPR)
▫ Crash detection
3

4. Problem Statement
• Detection Problem
▫ Presence and absence of vehicle
▫ No background information
• Design of Autonomous System for Traffic
▫ Detection
▫ Classification
▫ Display
4

5. Proposed Solution
• Detection Problem
▫ Background Modeling
𝐵𝑖 = 𝛼 × 𝐵𝑜 + 1 − 𝛼 𝐼𝑖, 0 ≤ 𝛼 ≤ 1 (1)
5

6. Vehicle Detection
𝑃𝑖 𝑘 𝐿 =
𝑛 𝐿
𝑁
(2)
𝑃𝑖+1
/
𝑘 𝐿 =
𝑛 𝐿
/
𝑁
(3)
∆𝑃𝐿 = 𝑃𝑖+1
/
𝑘 𝐿 − 𝑃𝑖 𝑘 𝐿 (4)
𝑇 = 𝑁 𝐿=0
255
∆𝑃𝐿 (5)
To simplify calculations, we make a use of histogram and relate
it with the above equations:
ℎ𝑖 𝑘 𝐿 = 𝑛 𝐿 (6)
ℎ𝑖 𝑘 𝐿 = 𝑁 × 𝑃𝑖 𝑘 𝐿 (7)
6

7. Cont.
𝑃𝑖+1
/
𝑘 𝐿 − 𝑃𝑖 𝑘 𝐿 =
| ℎ 𝑖 𝑘 𝐿 − ℎ 𝑖
/
𝑘 𝐿 |
𝑁
(8)
∆𝑃𝐿 =
| ℎ 𝑖 𝑘 𝐿 − ℎ 𝑖
/
𝑘 𝐿 |
𝑁
(9)
𝑇 =
1
𝑁 𝐿=0
255
| ℎ𝑖 𝑘 𝐿 − ℎ𝑖+1
/
𝑘 𝐿 | (10)
𝑉𝑖 =
1, 𝑇 > 𝑡
0, 𝑒𝑙𝑠𝑒
(11)
7

8. System Design
• Vehicle Detection System (VDS)
• Vehicle Counting and Classification System
(VCCS)
• Traffic Signals Control System (TSCS)
• Data Display System (DDS)
8

9. Cont.
• System Interconnection
9

10. VDS
• Probability Based Vehicle Detection (PBVD) Algorithm
▫ Frame acquisition
▫ Vehicle appearance
𝑇 =
1
𝑁 𝐿=0
255
| ℎ𝑖 𝑘 𝐿 − ℎ𝑖+1
/
𝑘 𝐿 | (10)
𝑉𝑖 =
1, 𝑇 > 𝑡
0, 𝑒𝑙𝑠𝑒
(11)
▫ Vehicle extraction
▫ Maximum area calculation
10

11. VCCS
• Vehicle labeling
• Classification of vehicles
▫ Small
▫ Medium
▫ Large
• Send vehicle statistics to
▫ TSCS
▫ DDS
11

12. TSCS
• Vehicle comparison for both lanes
• If same number of vehicles
▫ Assign equal timing to both lanes
12

13. DDS
• Display total number of vehicles
• Number of pixels each vehicle contain
• Vehicle category
• Green signal for road
13

14. Experimental Results
• Background update parameter
▫ α = 1 × 10-3
• Threshold for vehicle appearance
▫ t = 300
14

15. Cont..
Prototype of Proposed System
15

16. Cont.
Data Display System (DDS)
16

17. Cont.
a) Image of empty road b) image of the road containing car c) image
enhancement after median filtering d) image after frame subtraction
17

18. Cont.
a) Image after edge detection b) Binary dilation c) Binary Hole Filling d)
Binary Erosion
18

19. Cont.
a) Area Calculation b) Final Result (Detected Objects)
19

20. Cont.
Current frame
20

21. Cont.
Frame subtraction
21

22. Cont.
Edge detection and binary dilation
22

23. Cont.
Resultant image after binary hole filling,
erosion and area calculation
23

24. Cont.
Final result
24

25. Cont..
0 50 100 150 200 250
0
5
10
15
20
25
30
Pixel intensities
Absolutedifferene
Spred after absolute histogram subtraction
Spread after absolute histogram subtraction
25

26. Conclusion
• Traffic control system for
▫ Monitoring
▫ Control of intercity traffic
• Prototype testing
▫ Accurate results
• Real time testing
▫ Satisfactory results
▫ Morphological operators need control
▫ Need of restoration techniques
26

27. Question
27