1. A video-based real-time vehicle counting system using adaptive background method 2008 IEEE International Conference on Signal Image Technology and Internet Based Systems 組員：李瑋育，林立成，薩如鳴 指導教授：吳宗憲 授課教授：連震杰 1

2. Outline  Introduction  Adaptive Background Estimation  Segmentation  Background subtraction  Shadow elimination  Counting  Experimental Results  Conclusions  Demonstration 2

3. Introduction  Real time vehicle detection and counting system  Two main methods  The adaptive background estimation  Gaussian shadow elimination  Detector  Inductive Loop  Infra Red  Radar  Video based  More traffic information obtained  Easily installed  Scalable with progress in image processing techniques 3

4. Introduction cont. 4  Two goals  Robustness  Self-adaptive to variant scenes (daytime, nighttime, overcast, shadow, ghost, wind)  Performance  Low cost equipment of algorithm needed so that processing time can be reduced under a required time

5. System Overview 5

6. Adaptive Background Estimation  RGB 24 bits format video stream   luminance values of each pixel at time t of image   and absolute difference between  gained defined result of experiment  binaryzation operation between and  Due to sensor noise from camera and light fluctuation 6

7. Adaptive Background Estimation cont.  Q1: Why fig.(b) highlights the different edge instead of whole object?  A:Overlapping between two different moving frames. 7

8. Adaptive Background Estimation cont. 8   learning rate and controls the background speeds 

9. Adaptive Background Estimation cont. 9  Fig.(d) is the current image  Fig.(e) is the conjoined highlight pixel for each object with the ROI mask  Fig.(f) is the background model updated with the ROI mask

10. Background subtraction 10  A robust background model is needed for segment each frame into foreground and background objects   result of background subtraction , an absolute difference between and the background model

11. Background subtraction cont. 11  , and are automatic thresholds for each channel and evaluated by the background-subtracted image   Q2:Simple binaryzation isn’t sufficient to obtain a clear foreground so what should we do?  A:Morphological closing to fills the missing foreground pixels and morphological opening to remove the small isolated foreground pixels.

12. Shadow elimination 12  Foreground images contains  Moving objects  Shadows  May cause erroneous in vehicle counting  In the saturation channel shadow’s saturation is nearing road’s

13. Shadow elimination cont. 13  Saturation of background model has a Gaussian distribution  X:color Y:number of pixels  Band-stop filter to remove shadow   saturation of background model 

14. Counting 14  Count and register vehicle for each lane  “virtual detector” which were the rectangle region  FGI include only moving objects 

15. Experimental Results 15  Different hours in the day with fixed camera and resolution set at 320x240  Written on C++ and executed on standard PC  Scene 1: daytime, obvious shadows, camera faces the headlight  Scene 2: daytime, cloudy day, non obvious shadows, camera faces the tail-light  Scene 3: daytime, cloudy day, non obvious shadows, camera faces the headlight  Scene 4: nighttime, non street lamp, camera faces the headlight

16. Experimental Results cont. 16  Q3:Why camera faces the light leads a better results?  A: In the case of head-light heading, we can have better segmentation due to the better difference amid the pixels of foreground object and the background, hereby we can have the better result in the shadow elimination output.

17. Conclusions 17  Detect and count vehicles in complex scenes  Resolves relatively well various troublesome situation  Shadows  Not able to recognize vehicle types  Lorry-driver  Car  Motorcycle  Implementing vehicle classification for improving the statistic function

18. Demonstration 18  Binary motion 1.avi 1.avi  Binary motion mask computed using the frame differencing algorithm  Adaptive Background 2.avi 2.avi  Background model updated with the binary motion mask  Adaptive Background with rectangle region 3.avi 3.avi  Background model updated with the ROI mask  Adaptive Background Compare 4.avi 4.avi  比較利用 ROI mask 和沒有利用 ROI mask 的差別，可以明顯的看到利用 ROI mask 所更新 的背景影像會比沒有利用 ROI mask 來的清楚，因為可以整齊的清除掉殘留的物體移 動軌跡。  Background subtraction 5.avi 5.avi  利用 ROI mask 所得到的背景影像作 Background subtraction ，可以清楚的取出移動物體。