1. An Adaptive Windowing Prediction Algorithm for Vehicle Speed Estimation Tun-Wen Pai and Wen-Jung Juang Dept. of CS, National Taiwan Ocean University, Taiwan Lee-Lyi Wang Dept. of EE, Tung-Nan Institute of Technology,Taiwan

2. Outlines Motivation Motivation System Configuration and Assumptions System Configuration and Assumptions System Algorithms System Algorithms –Modules in Phase I –Modules in Phase II Simulation Results and Conclusions Simulation Results and Conclusions Future Works Future Works

3. Motivation

4. Motivation

5. Motivation

6. Basic Assumptions 1.The speed is finite and its value is nonnegative. 2.The speed and direction cannot change suddenly. 3.Drive in the permitted direction on the road. 4.The size is realized from a know distribution 5.The height of CCD and the distance between the CCD and Base line on screen is known.

7. System Configuration

8. Modules in Phase I To acquire video image sequences To acquire video image sequences –From road-side CCTV –320X240 pixels –Fifteen frames per second Perform CCD noise margin calibration Perform CCD noise margin calibration –Tolerant parameter m –Tolerant parameter ε m

9. Modules in Phase I (cont.) Moving Object Detection Moving Object Detection g i (x,y)={ |f i (x,y)-f i-1 (x,y) | ≧ εm } AND { |f i+1 (x,y)-f i (x,y) | ≧ εm } { |f i+1 (x,y)-f i (x,y) | ≧ εm }

10. Moving Object Detection Example ( i-1 th frame : f i-1 )

11. Moving Object Detection Example ( i th frame : f i )

12. Moving Object Detection Example ( i+1 th frame : f i+1 )

13. Moving Object Detection Example ( g i )

14. Morphological operations Dilation:Erosion: Structuring Element : 3x5 square element ∩

15. Morphological example

16. BCC and Region Growing Block size more then 5x5 we see it as “seed” Block size more then 5x5 we see it as “seed” Region growing criteria: Region growing criteria: –1. different not more then 2( ε m ) –2. pixel add to region at least one of his 8- neighboring pixel previously included in the region. –Bounding Box size larger than 5x5 we see it as candidate vehicle objects.

17. BCC and Region Growing Example

18. BCC and Region Growing Example(cont.)

19. Modules in Phase II AWP Block Matching AWP Block Matching Inverse Perspective Transformation Inverse Perspective Transformation Statistical Analysis Statistical Analysis

20. Adaptive Windowing Prediction (AWP) Block Matching Algorithm (BMA) Block Matching Algorithm (BMA) Mean Absolute Difference (MAD) Mean Absolute Difference (MAD) Uni-direction problem Uni-direction problem Uni-model error surface Uni-model error surface

21. Uni-model error surface

22. AWP algorithm

23. AWP algorithm (cont.)

24. AWP algorithm summarized 1.Use full search found initial moving distance 2.MAD for predefined adaptive checking point set 3.Find a temporary minimal MAD point 4.Checking the 4-neighbor, if all MAD measured then stop, else calculate un-measured neighbors 5.If new MAD value greater then old MAD then stop else replace the temporary minimal MAD point else replace the temporary minimal MAD point 6.Go to step3,until AWP found minimal MAD or until moving object vanished from screen 7.Use look-up table to obtain speed, and provide pixel distance to next coming frame

25. Full Search BMA Initial Moving Distance Adaptive Checking Point set MAD(V) Temporary Minimal MAD p(i,j)={min(MAD(q)):q V} 4-neighboring points MAD measurement Minimal MAD ? Measuring Moving Distance Look-up Table Next coming frame No Yes

26. Partial Look-up table 12345678910 806142127344149576370 1006121824313743495663 1205111622283339445056 1405101521253035394651 160491319232732364146 Distance Position

27. Inverse Perspective Transformation

28. Inverse Perspective Transformation (cont.)

29. Statistical Analysis Obtain a set of sequential speed data for each observed vehicle,we can get a mean speed for each time slot. Obtain a set of sequential speed data for each observed vehicle,we can get a mean speed for each time slot. So we can obtain it form www or wap So we can obtain it form www or wap

30. Simulation Examples The mean value of tolerant margin for CCD is 2.45(with gray-level scale[0,255]) The mean value of tolerant margin for CCD is 2.45(with gray-level scale[0,255]) There are vehicle objects detected from frame number 450 to 600 There are vehicle objects detected from frame number 450 to 600 The average speed is about 30km/hr The average speed is about 30km/hr Only 8.6% of the computations(in terms of check points) are required in AWP algorithm with respect to the Full Search algorithm Only 8.6% of the computations(in terms of check points) are required in AWP algorithm with respect to the Full Search algorithm

31. Examples Original VideoDetected Vehicles

32. Future Work Difference Weather Condition Difference Weather Condition Size distribution Size distribution CCD height and base line distance CCD height and base line distance Morphological structure element Morphological structure element