1. Bo QIN, Zongshun MA, Zhenghua FANG, Shengke WANG Computer-Aided Design and Computer Graphics, 2007 10th IEEE International Conference on, p 202 - 207 Presenter : Jia – Hong Zeng Advisor : Dr. Yen – Ting Chen Date : 2013.11.20 Fast Detection of Vehicles Based-on the Moving Region 1

2. OUTLINE Introduction Purpose Materials and Methods Results Conclusion 2

3. INTRODUCTION(1/5)  The precise location and tracking of the moving vehicles is an important part in Intelligent Transportation System(ITS), and it still encounters many difficulties, such as : Shadows Camera noises in real-world traffic scenes Changes in lighting Weather conditions, etc. 3

4. INTRODUCTION(2/5)  The shadows will influence the vehicle detection process and cause great inaccuracy even error in the following process such as Precise positioning Extraction of key part 4

5. INTRODUCTION(3/5)  In the area of motion detection and tracking, most researches have been devoted to solving several problems : Background extraction Background updating Shadow elimination Edge detection 5

6. INTRODUCTION(4/5)  Shadow detection technique can be classified into two groups : Property-based Geometry Brightness Color Model-based 6

7. INTRODUCTION(5/5)  The main process is done in three steps : Moving region detection Shadow detection Edge detection 7

8. PURPOSE This paper presents a new method for detecting vehicles. Self-adaptive background Shadow detection fast Eliminate the shadow 8

9. MATERIALS AND METHODS(1/11) The system proposed for the shadow detection of moving objects consists of two parts : Pretreatment Exact Vehicle Detection 9

10. MATERIALS AND METHODS(2/11)  Adaptive threshold on Gaussian model 10

11. MATERIALS AND METHODS(3/11) Let be the histogram probability of a difference value(d). The threshold value e is determined by a fitting criterion defined as: 11

12. MATERIALS AND METHODS(4/11)  Quick self-adaptive background updating Extract Background Model(BM) using the selective averaging method. Extract Moving Region(MR) with the binary object mask image. Take the difference between Current Image(CI) and Current Background(CB) image to determine which area should be updated or not. 12

13. MATERIALS AND METHODS(5/11) 13

14. MATERIALS AND METHODS(6/11)  Constructing the Background Model(BM) The experiment demonstrates that letting n be 100 can bring a good result. 14

15. MATERIALS AND METHODS(7/11)  Acquiring the Moving Region(MR) 15

16. MATERIALS AND METHODS(8/11)  Self-adaptive background updating α is a weight factor, assigned to the current and instantaneous background. The weight has been empirically determined to be 0.1 can give the best result of experiment. 16

17. MATERIALS AND METHODS(9/11)  Shadow detection based on improved HSV color space approach ρ(x, y) is the reflectance of the object surface, and E(x, y) is the irradiance and is computed as follows: 17

18. MATERIALS AND METHODS(10/11) 18

19. MATERIALS AND METHODS(11/11)  Detection of the shadows 19

20. RESULTS(1/5) The algorithm presented in this paper has a very good effect in recognizing the certain parts especially those adjacent to the shadows. Figure 3. Two raw images from the sequences 20

21. RESULTS(2/5) Figure 4. The background updating After the algorithm, construct the background model and this model can updating through self-adaptive. 21

22. RESULTS(3/5) Figure 5. The edge of the moving regionFigure 6. The edges of the shadow 22

23. RESULTS(4/5) Figure 7. The subtraction of the edge of the moving region without the shadow 23

24. RESULTS(5/5) Figure 8. The detection of the exact vehicle in the real image 24

25. CONCLUSION Presented a new and fast vehicle detecting system capable of robustly working under most circumstances. The system is general enough to be capable of detecting and classifying vehicles while requiring only minimal scene-specific parameters, which can be obtained through training. 25

26. CONCLUSION The exact detection of the vehicle object makes the location of the key part of the vehicle possible, which also found bases for the following steps such as classification or tracking of the vehicles. 26

27. THANKS FOR YOUR ATTENTION 27