1. Road Sign Recognition System Based on GentleBoost with Sharing Features
Jin-Yi Wu, Chien-Chung Tseng,Chun-Hao Chang,
Jenn-Jier James Lien*, Ju Chin Chen, Ching Ting Tu
ICSSE 2011

2. Outline Goal Method Experimental Result Future work Detection Module
Recognition Module
Experimental Result
Future work

3. Goal
Guide the driver to drive in the correct lane and at the right speed.
support the driver during the tedious task of remembering the large number of road signs.

4. Flowchart

5. Method: two modules Detection Module Recognition Module
Stage 1:Color-Based, finding sign candidates.
Stage 2:Shap-Based, Classification.
Recognition Module
Stage 1: GenteBoost with Sharing Features
Stage 2: Rotation, scale, translation invariant

6. Detection Module Stage 1: Color-Based Segmentation
Road signs are designed using colors to reflect it’s message.
These colors stand out from the environment.

7. HIS color space
hue saturation intensity (HSI) domain are sufficient to isolate road signs in a scene.
[4] S. M. Bascon, S. L. Arroyo, P. G. Jimenez, H. G. Moreno, F. L. Ferreras, "Road-Sign Detection and Recognition Based on Support Vector Machines", IEEE Transaction Intelligent Transportation Systems, vol. 8, no. 2, pp , 2007.

8. Threshold
the response to varying wavelength and intensity of standard imaging is nonlinear and interdependent.
The database GRAM and other image are used to train the suitable threshold.

9. Candidate selection Each connected object is called a blob.
A candidate blob must laeger than 30x30.
aspect ratio is delimited between 1.9 and 1/1.9(suggested in [4])
[4] S. M. Bascon, S. L. Arroyo, P. G. Jimenez, H. G. Moreno, F. L. Ferreras, "Road-Sign Detection and Recognition Based on Support Vector Machines", IEEE Transaction Intelligent Transportation Systems, vol. 8, no. 2, pp , 2007.

10. Detection Module Stage 2: Shape-Based Classification
Then Distance to borders (DtBs) feature and linear Support Vector Machine (SVM) are used to classify the shape of the blobs as [4].
[4] S. M. Bascon, S. L. Arroyo, P. G. Jimenez, H. G. Moreno, F. L. Ferreras, "Road-Sign Detection and Recognition Based on Support Vector Machines", IEEE Transaction Intelligent Transportation Systems, vol. 8, no. 2, pp , 2007.

11. linear SVM Database GRAM and other image. DtBs result.
Classify the blobs into a certain shape, i.e. circular, triangular, rectangular shapes.

12. Method: two modules Detection Module Recognition Module
Stage 1:Color-Based, finding sign candidates.
Stage 2:Shap-Based, Classification.
Recognition Module
Stage 1: GenteBoost with Sharing Features
Stage 2: Rotation, scale, translation invariant

13. Recognition Module Stage 1: GenteBoost with Sharing Features
Use weak classifiers to form a stronger classifier.

14. Road sign database 30 x 30 pixel. 108 road signs:
48 red triangular signs
36 red circular signs
15 blue circular signs
9 blue rectangular signs

15. Chromatic parts(1/2) 20x20-pixel. 5 types of red circular.
used for ensuring the existence of the road signs.
type1
type2
Type3,4,5

16. Chromatic parts(2/2)
if the chromatic part matches one of the types, we lower the threshold for the according type in RST-Invariant template matching due to the high probability that road sign in the same type may appear.

17. Rotation, scale, translation invariant (RST-invariant)
Red road signs:
Simply match the middle part of candidate blob(20x20-pixel).
The thresholds is adjusted by the result from the GentleBoost detector.(only red circular signs)
Blue road signs:
Simply match the complete candidate blob (30x30-pixel).

18. Step 1: Circular sampling filter (Cifi)
R is the radius of the template.
Corr = correlation
Ti is ith templates with the same shape
If the Corr value is larger than a threshold tc, the template Ti is passed to second step, otherwise, Ti will be discard.
C(x, y)={C(x, y, r), r = 1 to R}

19. Step 2: Radial sampling filter (Rafi)
α is inclianation of Radial line, l is length of Radial line.
“cshiftj” means circular shifting j positions of the argument vector.
If Corr value is larger than a threshold tr, the template Tk will be rotated with the corresponding angle and passed to the final step.
R(x,y) = R(x,y,α), α = 0 ~ 360}

20. Step 3: template matching filter step
Corresponding with template which pass the step2 ?
There is no detail mention in this paper ?

21. Thresholds tc=0.9, tr=0.9, and tm=0.8
tc=0.5, tr=0.5,and tm=0.45 for the corresponding type of the candidate blob.

22. Experimental result(1/2)
The detection rate and the false alarm rate for road signs in GRAM database, which is also used in [27] and [28], is 80.4% and 45.4, respectively.
632 images for Experimental.
[27] P. Gil-Jimenez, S. Lafuente-Arroyo, H. Gomez-Moreno, F. Lopez- Ferreras, and S. Maldonado-Bascon,” Traffic Sign Shape Classification Evaluation II : FFT Applied to The Sognature of Blobs,” in Proceedings of IEEE Intelligent Vehicles Symposium, pp , 2005.
[28] S. Lafuente-Arroyo, P. Gil-Jimenez, R. Maldonado-Bascon ,” Traffic Sign Shape Classification Evaluation I : SVM Using Distance to Broders,” in Proceedings of IEEE Intelligent Vehicles. Symposium, pp , 2005.

23. Experimental result(2/2)

24. This work…
able to accurately classify different shapes of road signs in difficult conditions.(rotations, scaling, translations, and even partial occlusions.)
can run in almost real-time with 720x480-pixel image with average 12 fps on a 3.0-GHz CPU.

25. Future work to improvements
Same false alarm usually will not appear in adjacent frames.
Using different feature rather than DtBs in shape classification.
Extended to detect some other kinds of signboards such as signs of gas station or convenient shop